Search results for: Corporate credit rating prediction

Authors: Huiming Peng, Jianguo Wen, Hongwei Li, Jeff Chang, Xiaobo Zhou

Abstract:

NFκB activation plays a crucial role in anti-apoptotic responses in response to the apoptotic signaling during tumor necrosis factor (TNFa) stimulation in Multiple Myeloma (MM). Although several drugs have been found effective for the treatment of MM by mainly inhibiting NFκB pathway, there are no any quantitative or qualitative results of comparison assessment on inhibition effect between different single drugs or drug combinations. Computational modeling is becoming increasingly indispensable for applied biological research mainly because it can provide strong quantitative predicting power. In this study, a novel computational pathway modeling approach is employed to comparably assess the inhibition effects of specific single drugs and drug combinations on the NFκB pathway in MM, especially the prediction of synergistic drug combinations.

Keywords: Computational modeling, drug combination, inhibition effect, multiple myeloma, NFkB pathway.

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Authors: Doseong Eom, Jeongmin Kim, Kwang Ryel Ryu

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Large exhibition halls require a lot of energy to maintain comfortable atmosphere for the visitors viewing inside. One way of reducing the energy cost is to have thermal energy storage systems installed so that the thermal energy can be stored in the middle of night when the energy price is low and then used later when the price is high. To minimize the overall energy cost, however, we should be able to decide how much energy to save during which time period exactly. If we can foresee future energy load and the corresponding cost, we will be able to make such decisions reasonably. In this paper, we use machine learning technique to obtain models for predicting weather conditions and the number of visitors on hourly basis for the next day. Based on the energy load thus predicted, we build a cost-optimal daily operation plan for the thermal energy storage systems and cooling and heating facilities through simulation-based optimization.

Keywords: Building energy management, machine learning, simulation-based optimization, operation planning.

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Authors: K. Ö. Kartal, L. Maul, M. Hägele

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With the development of new technologies come additional opportunities for the founding of companies and new markets to be created. The barriers to entry are lowered and technology makes old business models obsolete. Incumbent companies have to be adaptable to this quickly changing environment. They have to start the process of digital maturation and they have to be able to adapt quickly to new and drastic changes that might arise. One of the biggest barriers for organizations in order to do so is their culture. This paper shows the core elements of a corporate culture that supports the process of digital maturation in incumbent organizations. Furthermore, it is explored how ideation and innovation can be used in a strategy in order to facilitate these core elements of culture that promote digital maturity. Focus areas are identified for the design of ideation strategies, with the aim to make the facilitation and incitation process more effective, short to long term. Therefore, one in-depth case study is conducted with data collection from interviews, observation, document review and surveys. The findings indicate that digital maturity is connected to cultural shift and 11 relevant elements of digital culture are identified which have to be considered. Based on these 11 core elements, five focus areas that need to be regarded in the design of a strategy that uses ideation and innovation to facilitate the cultural shift are identified. These are: Focus topics, rewards and communication, structure and frequency, regions and new online formats.

Keywords: Digital transformation, innovation management, ideation strategy, creativity culture, change.

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Authors: G.H. Farrahi, S.A. Faghidian, D.J. Smith

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A thin layer on the component surface can be found with high tensile residual stresses, due to turning operations, which can dangerously affect the fatigue performance of the component. In this paper an analytical approach is presented to reconstruct the residual stress field from a limited incomplete set of measurements. Airy stress function is used as the primary unknown to directly solve the equilibrium equations and satisfying the boundary conditions. In this new method there exists the flexibility to impose the physical conditions that govern the behavior of residual stress to achieve a meaningful complete stress field. The analysis is also coupled to a least squares approximation and a regularization method to provide stability of the inverse problem. The power of this new method is then demonstrated by analyzing some experimental measurements and achieving a good agreement between the model prediction and the results obtained from residual stress measurement.

Keywords: Residual stress, Limited measurements, Inverse problems, Turning process.

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Authors: Moaz H. Ali, M. N. M. Ansari, Pang Jing Shen

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In present work, prediction the effect of nose radius, rz (mm) on the equivalent strain (PEEQ) and surface finish during the machining of titanium alloy (Ti-6Al-4V) through orthogonal cutting process. The results were performed at several of the nose radiuses, rz (mm) while the cutting speed, vc (m/min), feed rate, f (mm/tooth) and depth of cut, d (mm) were remained constant. The equivalent plastic strain (PEEQ) was estimated by using finite element modeling (FEM) and applied through ABAQUS/EXPLICIT software. The simulation results led to conclude that the equivalent plastic strain (PEEQ) was increased and surface roughness (Ra) decreased when increasing nose radius, rz (mm) during the machining of titanium alloy (Ti–6Al–4V) in dry cutting conditions.

Keywords: Finite element modeling (FEM), nose radius, plastic strain (PEEQ), titanium alloy (Ti-6Al-4V).

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Authors: F. Rasi Maezabadi, M. Masdari, M. R. Soltani

Abstract:

Series of experimental tests were conducted on a section of a 660 kW wind turbine blade to measure the pressure distribution of this model oscillating in plunging motion. In order to minimize the amount of data required to predict aerodynamic loads of the airfoil, a General Regression Neural Network, GRNN, was trained using the measured experimental data. The network once proved to be accurate enough, was used to predict the flow behavior of the airfoil for the desired conditions. Results showed that with using a few of the acquired data, the trained neural network was able to predict accurate results with minimal errors when compared with the corresponding measured values. Therefore with employing this trained network the aerodynamic coefficients of the plunging airfoil, are predicted accurately at different oscillation frequencies, amplitudes, and angles of attack; hence reducing the cost of tests while achieving acceptable accuracy.

Keywords: Airfoil, experimental, GRNN, Neural Network, Plunging.

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Authors: Amgad A. Farghal, Dina I. El Desouki

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Energy consumption and users’ satisfaction were compared in three LEED certified office buildings in turkey and an office building in Egypt. The field studies were conducted in summer 2012. The measured environmental parameters in the four buildings were indoor air temperature, relative humidity, CO2 percentage and light intensity. The traditional building is located in Smart Village in Abu Rawash, Cairo, Egypt. The building was studied for 7 days resulting in 84 responds. The three rated buildings are in Istanbul; Turkey. A Platinum LEED certified office building is owned by BASF and gained a platinum certificate for new construction and major renovation. The building was studied for 3 days resulting in 13 responds. A Gold LEED certified office building is owned by BASF and gained a gold certificate for new construction and major renovation. The building was studied for 2 days resulting in 10 responds. A silver LEED certified office building is owned by Unilever and gained a silver certificate for commercial interiors. The building was studied for 7 days resulting in 84 responds. The results showed that all buildings had no significant difference regarding occupants’ satisfaction with the amount of lighting, noise level, odor and access to the outdoor view. There was significant difference between occupants’ satisfaction in LEED certified buildings and the traditional building regarding the thermal environment and the perception of the general environment (colors, carpet and decoration. The findings suggest that careful design could lead to a certified building that enhances the thermal environment and the perception of the indoor environment leading to energy consumption without scarifying occupants’ satisfaction.

Keywords: Energy consumption, occupants’ satisfaction, rating systems.

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Authors: Inamullah Bhatti, Ahsanullah Baloch, Khadija Qureshi

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The objective of this research is to examine the shear thinning behaviour of mixing flow of non-Newtonian fluid like toothpaste in the dissolution container with rotating stirrer. The problem under investigation is related to the chemical industry. Mixing of fluid is performed in a cylindrical container with rotating stirrer, where stirrer is eccentrically placed on the lid of the container. For the simulation purpose the associated motion of the fluid is considered as revolving of the container, with stick stirrer. For numerical prediction, a time-stepping finite element algorithm in a cylindrical polar coordinate system is adopted based on semi-implicit Taylor-Galerkin/pressure-correction scheme. Numerical solutions are obtained for non-Newtonian fluids employing power law model. Variations with power law index have been analysed, with respect to the flow structure and pressure drop.

Keywords: finite element simulation, mixing fluid, rheology, rotating flow, toothpaste

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Authors: Djarot B. Darmadi

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The wider growing Finite Element Method (FEM) application is caused by its benefits of cost saving and environment friendly. Also, by using FEM a deep understanding of certain phenomenon can be achieved. This paper observed the role of material properties and volumetric change when Solid State Phase Transformation (SSPT) takes place in residual stress formation due to a welding process of ferritic steels through coupled Thermo- Metallurgy-Mechanical (TMM) analysis. The correctness of FEM residual stress prediction was validated by experiment. From parametric study of the FEM model, it can be concluded that the material properties change tend to over-predicts residual stress in the weld center whilst volumetric change tend to underestimates it. The best final result is the compromise of both by incorporates them in the model which has a better result compared to a model without SSPT.

Keywords: Residual stress, ferritic steels, SSPT, coupled-TMM.

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Authors: Zarita Zainuddin, Ong Pauline, C. Ardil

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Diabetes Mellitus is a chronic metabolic disorder, where the improper management of the blood glucose level in the diabetic patients will lead to the risk of heart attack, kidney disease and renal failure. This paper attempts to enhance the diagnostic accuracy of the advancing blood glucose levels of the diabetic patients, by combining principal component analysis and wavelet neural network. The proposed system makes separate blood glucose prediction in the morning, afternoon, evening and night intervals, using dataset from one patient covering a period of 77 days. Comparisons of the diagnostic accuracy with other neural network models, which use the same dataset are made. The comparison results showed overall improved accuracy, which indicates the effectiveness of this proposed system.

Keywords: Diabetes Mellitus, principal component analysis, time-series, wavelet neural network.

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Authors: Supriya Pal, Kalyan Adhikari, Somnath Mukherjee, Sudipta Ghosh

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This study presents the application of artificial neural network for modeling the phenolic compound migration through vertical soil column. A three layered feed forward neural network with back propagation training algorithm was developed using forty eight experimental data sets obtained from laboratory fixed bed vertical column tests. The input parameters used in the model were the influent concentration of phenol(mg/L) on the top end of the soil column, depth of the soil column (cm), elapsed time after phenol injection (hr), percentage of clay (%), percentage of silt (%) in soils. The output of the ANN was the effluent phenol concentration (mg/L) from the bottom end of the soil columns. The ANN predicted results were compared with the experimental results of the laboratory tests and the accuracy of the ANN model was evaluated.

Keywords: Modeling, Neural Networks, Phenol, Soil media

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Authors: Mohammed Bilal

Abstract:

The precise prediction of aerodynamic behavior is necessary for the design and optimization of airfoils for a variety of applications. Turbulence, a phenomenon of complex and irregular flow, significantly affects the aerodynamic properties of airfoils. Therefore, turbulence modeling is essential for accurately predicting the behavior of airfoils in simulations. This study investigates five commonly employed turbulence models: Spalart-Allmaras (SA) model, k-epsilon model, k-omega model, Reynolds Stress Model (RSM), and Large Eddy Simulation (LES) model. The paper includes a comparison of the models' precision, computational expense, and applicability to various flow conditions. The strengths and weaknesses of each model are highlighted, allowing researchers and engineers to make informed decisions regarding simulations of specific airfoils. Unquestionably, the continuous development of turbulence modeling will contribute to further improvements in airfoil design and optimization, which will be advantageous to numerous industries.

Keywords: Computational fluid dynamics, airfoil, turbulence, aircraft.

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Authors: Gleda Kutrolli, Maksi Kutrolli, Etjon Meco

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SARS-CoV-2 virus is currently one of the most infectious pathogens for humans. It started in China at the end of 2019 and now it is spread in all over the world. The origin and diffusion of the SARS-CoV-2 epidemic, is analysed based on the discussion of viral phylogeny theory. With the aim of understanding the spread of infection in the affected countries, it is crucial to modelize the spread of the virus and simulate its activity. In this paper, the prediction of coronavirus outbreak is done by using SIR model without vital dynamics, applying different numerical technique solving ordinary differential equations (ODEs). We find out that ABM and MRT methods perform better than other techniques and that the activity of the virus will decrease in April but it never cease (for some time the activity will remain low) and the next cycle will start in the middle July 2020 for Norway and Denmark, and October 2020 for Sweden, and September for Finland.

Keywords: Forecasting, ordinary differential equations, SARS-CoV-2 epidemic, SIR model.

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Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

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Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: Microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks.

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Authors: Chokri Slim

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The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.

Keywords: Neuro-fuzzy, Extended Kalman filter, nonlinear systems, financial time series.

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Authors: Kelechi E. Ezeji

Abstract:

Instruction towards acquiring skills in the use of Computer Aided Design technologies has become a vital part of architectural education curriculum in the digital era. Its implementation, however, requires deployment of extra resources to build new infrastructure, acquisition and maintenance of new equipment, retraining of staff and recruitment of new ones who are knowledgeable in this area. This study sought to examine the impact that ownership structure of Nigerian universities had on provision of staff and infrastructure for implementing computer aided design curriculum with a view to developing a framework for the evaluation for appropriate implementation by the institutions. Survey research design was employed. The focus was on departments of architecture in universities in south-east Nigeria accredited by the National Universities Commission. Data were obtained in the areas of infrastructure and personnel for CAD implementation. A multi-stage stratified random sampling method was adopted. The first stage of stratification involved the accredited departments. Random sampling by balloting was then carried out. At the second stage, sampling size formulae was applied to obtain respondents’ number. For data analysis, analysis of variance tool for testing differences of means was used. With ρ < 0.5, the study found that there was significant difference between private-funded, state-funded and federal-funded departments of architecture in the provision of personnel and infrastructure. The implications of these findings were that for successful implementation leading to attainment of CAD proficiency to occur in every institution regardless of ownership structure, minimum evaluation guidelines needed to be set. A regular comparison of implementation in institutions was recommended as a means of rating performance. This will inform better interaction with those who consistently show weakness to challenge them towards improvement.

Keywords: Computer-aided design, curriculum, funding, infrastructure.

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Authors: Dalvir Singh, Somnath Chattopadhyaya

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During welding or flame cutting of metals, the prediction of heat affected zone (HAZ) is critical. There is need to develop a simple mathematical model to calculate the temperature variation in HAZ and derivative analysis can be used for this purpose. This study presents analytical solution for heat transfer through conduction in mild steel plate. The homogeneous and nonhomogeneous boundary conditions are single variables. The full field analytical solutions of temperature measurement, subjected to local heating source, are derived first by method of separation of variables followed with the experimental visualization using infrared imaging. Based on the present work, it is suggested that appropriate heat input characteristics controls the temperature distribution in and around HAZ.

Keywords: Conduction Heat Transfer, Heat Affected Zone (HAZ), Infra-Red Imaging, Numerical Method, Orthogonal Function, Plasma Arc Cutting, Separation of Variables, Temperature Measurement.

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Authors: Mingook Lee, Sungjoo Lee

Abstract:

As technology-based service industries grow drastically worldwide; companies are recognizing the importance of market preoccupancy and have made an effort to capture a large market to gain the upper hand. To this end, a focus on patents can be used to determine the properties of a technology, as well as to capture advantages in technical skills, in comparison with the firm’s competitors. However, technology-based services largely depend not only on their technological value but also their economic value, due to the recognized worth that is passed to a plurality of users. Thus, it is important to determine whether there are any competitors in the target areas and what services they provide in any field. Despite this importance, little effort has been made to systematically benchmark competitors in order to identify business opportunities. Thus, this study aims to not only identify each position of technology-centered service companies in complex market dynamics, but also to discover new business opportunities. For this, we try to consider both technology and market environments simultaneously by utilizing patent data as a representative proxy for technology and trademark dates as an index for a firm’s target goods and services. Theoretically, this is one of the earliest attempts to combine patent data and trademark data to analyze corporate strategies. In practice, the research results are expected to be used as a decision criterion to diagnose the economic value that companies can obtain by entering the market, as well as the technological value to be passed onto their customers. Thus, the proposed approach can be useful to support effective technology and business strategies in a firm.

Keywords: Business opportunity, patent, Portfolio analysis, trademark.

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Authors: Musa H. Arslan, Murat Ceylan, Tayfun Koyuncu

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In this study, an Artificial Neural Network (ANN) analytical method has been developed for analyzing earthquake performances of the Reinforced Concrete (RC) buildings. 66 RC buildings with four to ten storeys were subjected to performance analysis according to the parameters which are the existing material, loading and geometrical characteristics of the buildings. The selected parameters have been thought to be effective on the performance of RC buildings. In the performance analyses stage of the study, level of performance possible to be shown by these buildings in case of an earthquake was determined on the basis of the 4-grade performance levels specified in Turkish Earthquake Code-2007 (TEC-2007). After obtaining the 4-grade performance level, selected 23 parameters of each building have been matched with the performance level. In this stage, ANN-based fast evaluation algorithm mentioned above made an economic and rapid evaluation of four to ten storey RC buildings. According to the study, the prediction accuracy of ANN has been found about 74%.

Keywords: Artificial neural network, earthquake, performance, reinforced concrete.

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Authors: D. Uma Devi, P. Seetha Ramaiah

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Voting algorithms are extensively used to make decisions in fault tolerant systems where each redundant module gives inconsistent outputs. Popular voting algorithms include majority voting, weighted voting, and inexact majority voters. Each of these techniques suffers from scenarios where agreements do not exist for the given voter inputs. This has been successfully overcome in literature using fuzzy theory. Our previous work concentrated on a neuro-fuzzy algorithm where training using the neuro system substantially improved the prediction result of the voting system. Weight training of Neural Network is sub-optimal. This study proposes to optimize the weights of the Neural Network using Artificial Bee Colony algorithm. Experimental results show the proposed system improves the decision making of the voting algorithms.

Keywords: Voting algorithms, Fault tolerance, Fault masking, Neuro-Fuzzy System (NFS), Artificial Bee Colony (ABC)

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Authors: Surinder Deswal, D. V. S. Verma

Abstract:

Aeration by a plunging water jet is an energetically attractive way to effect oxygen-transfer than conventional oxygenation systems. In the present study, a new type of conical shaped plunging aeration device is fabricated to generate hollow inclined ined plunging jets (jet plunge angle of π/3 ) to investigate its oxygen transfer capacity. The results suggest that the volumetric oxygen-transfer coefficient and oxygen-transfer efficiency of the conical plunging jet aerator are competitive with other types of aeration systems. Relationships of volumetric oxygen-transfer coefficient with jet power per unit volume and jet parameters are also proposed. The suggested relationships predict the volumetric oxygentransfer coefficient within a scatter of ± 15% . Further, the application of Support Vector Machines on the experimental data revealed its utility in the prediction of volumetric oxygen-transfer coefficient and development of conical plunging jet aerators.

Keywords: Conical plunging jet, oxygen-transfer efficiency, support vector machines, volumetric oxygen-transfer coefficient.

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Authors: S. A. Naeini, A. Khalili

Abstract:

Nowadays, tunnels with different applications are developed, and most of them are related to subway tunnels. The excavation of shallow tunnels that pass under municipal utilities is very important, and the surface settlement control is an important factor in the design. The study sought to analyze the settlement and also to find an appropriate model in order to predict the behavior of the tunnel in Tehran subway line-3. The displacement in these sections is also determined by using numerical analyses and numerical modeling. In addition, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method is utilized by Hybrid training algorithm. The database pertinent to the optimum network was obtained from 46 subway tunnels in Iran and Turkey which have been constructed by the new Austrian tunneling method (NATM) with similar parameters based on type of their soil. The surface settlement was measured, and the acquired results were compared to the predicted values. The results disclosed that computing intelligence is a good substitute for numerical modeling.

Keywords: Settlement, subway line, FLAC3D, ANFIS method.

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Authors: Jun Wan, Zehua Chen, Yingwu Chen, Zhidong Bai

Abstract:

Classification is an interesting problem in functional data analysis (FDA), because many science and application problems end up with classification problems, such as recognition, prediction, control, decision making, management, etc. As the high dimension and high correlation in functional data (FD), it is a key problem to extract features from FD whereas keeping its global characters, which relates to the classification efficiency and precision to heavens. In this paper, a novel automatic method which combined Genetic Algorithm (GA) and classification algorithm to extract classification features is proposed. In this method, the optimal features and classification model are approached via evolutional study step by step. It is proved by theory analysis and experiment test that this method has advantages in improving classification efficiency, precision and robustness whereas using less features and the dimension of extracted classification features can be controlled.

Keywords: Classification, functional data, feature extraction, genetic algorithm, wavelet.

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Authors: Hany Alashwal, Safaai Deris, Razib M. Othman

Abstract:

Predicting protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been applied to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. Although it is easy to get a dataset of interacting proteins as positive examples, there are no experimentally confirmed non-interacting proteins to be considered as negative examples. Therefore, in this paper we solve this problem as a one-class classification problem using one-class support vector machines (SVM). Using only positive examples (interacting protein pairs) in training phase, the one-class SVM achieves accuracy of about 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with comparable accuracy to the binary classifiers that use artificially constructed negative examples.

Keywords: Bioinformatics, Protein-protein interactions, One-Class Support Vector Machines

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Authors: Zerarka Hizia, Akchiche Mustapha, Prunier Florent

Abstract:

The landslide of Ain El Hammam (AEH) is characterized by a complex geology and a high hydrogeology hazard. AEH's perpetual reactivation compels us to look closely at its triggers and to better understand the mechanisms of its evolution in mass and in depth. This study builds a numerical model to simulate the influencing factors such as precipitation, non-saturation, and pore pressure fluctuations, using Plaxis software. For a finer analysis of instabilities, we use Hill's criterion, based on the sign of the second order work, which is the most appropriate material stability criterion for non-associated elastoplastic materials. The results of this type of calculation allow us, in theory, to predict the shape and position of the slip surface(s) which are liable to ground movements of the slope, before reaching the rupture given by the plastic limit of Mohr Coulomb. To validate the numerical model, an analysis of inclinometer measures is performed to confirm the direction of movement and kinematic of the sliding mechanism of AEH’s slope.

Keywords: Landslide, second order work, precipitation, inclinometers.

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Authors: R. J. Chang

Abstract:

A cyclostationary Gaussian linearization method is formulated for investigating the time average response of nonlinear system under sinusoidal signal and white noise excitation. The quantitative measure of cyclostationary mean, variance, spectrum of mean amplitude, and mean power spectral density of noise are analyzed. The qualitative response behavior of stochastic jump and bifurcation are investigated. The validity of the present approach in predicting the quantitative and qualitative statistical responses is supported by utilizing Monte Carlo simulations. The present analysis without imposing restrictive analytical conditions can be directly derived by solving non-linear algebraic equations. The analytical solution gives reliable quantitative and qualitative prediction of mean and noise response for the Duffing system subjected to both sinusoidal signal and white noise excitation.

Keywords: Cyclostationary, Duffing system, Gaussian linearization, sinusoidal signal and white noise.

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Authors: Ε. Giovanis

Abstract:

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer after the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model in the out-of-sample period. The idea behind this approach is to propose a parametric regression with weighted variables in order to test for the statistical significance and the magnitude of the estimated autoregressive coefficients and simultaneously to estimate the forecasts.

Keywords: Autoregressive model, Error back-propagation Feed-Forward neural networks, , Gross Domestic Product

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Authors: E. Giovanis

Abstract:

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer of the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model. Moreover this technique can be used in Autoregressive-Moving Average models, with and without exogenous inputs, as also the training process with genetics algorithms optimization can be replaced by the error back-propagation algorithm.

Keywords: Autoregressive model, Feed-Forward neuralnetworks, Genetic Algorithms, Gross Domestic Product

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Authors: Yeqing Guan, Fen Yang

Abstract:

To learn about China-s future energy demand, this paper first proposed GM(1,1) model group based on recursive solutions of parameters estimation, setting up a general solving-algorithm of the model group. This method avoided the problems occurred on the past researches that remodeling, loss of information and large amount of calculation. This paper established respectively all-data-GM(1,1), metabolic GM(1,1) and new information GM (1,1)model according to the historical data of energy consumption in China in the year 2005-2010 and the added data of 2011, then modeling, simulating and comparison of accuracies we got the optimal models and to predict. Results showed that the total energy demand of China will be 37.2221 billion tons of equivalent coal in 2012 and 39.7973 billion tons of equivalent coal in 2013, which are as the same as the overall planning of energy demand in The 12th Five-Year Plan.

Keywords: energy demands, GM(1, 1) model group, least square estimation, prediction

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Authors: Danilo López, Edwin Rivas, Leyla López

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This paper presents the development of an algorithm that predicts the arrival of a secondary user (SU) to a base station (BS) in a cognitive network based on infrastructure, requesting a Best Effort (BE) or Real Time (RT) type of service with a determined bandwidth (BW) implementing neural networks. The algorithm dynamically uses a neural network construction technique using the geometric pyramid topology and trains a Multilayer Perceptron Neural Networks (MLPNN) based on the historical arrival of an SU to estimate future applications. This will allow efficiently managing the information in the BS, since it precedes the arrival of the SUs in the stage of selection of the best channel in CRN. As a result, the software application determines the probability of arrival at a future time point and calculates the performance metrics to measure the effectiveness of the predictions made.

Keywords: Cognitive radio, MLPNN, base station, prediction, best effort, real time.

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