**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**4903

# Search results for: General Regression Neural Network

##### 4903 A Combined Neural Network Approach to Soccer Player Prediction

**Authors:**
Wenbin Zhang,
Hantian Wu,
Jian Tang

**Abstract:**

An artificial neural network is a mathematical model inspired by biological neural networks. There are several kinds of neural networks and they are widely used in many areas, such as: prediction, detection, and classification. Meanwhile, in day to day life, people always have to make many difficult decisions. For example, the coach of a soccer club has to decide which offensive player to be selected to play in a certain game. This work describes a novel Neural Network using a combination of the General Regression Neural Network and the Probabilistic Neural Networks to help a soccer coach make an informed decision.

**Keywords:**
General Regression Neural Network,
Probabilistic Neural Networks,
Neural function.

##### 4902 Efficient System for Speech Recognition using General Regression Neural Network

**Authors:**
Abderrahmane Amrouche,
Jean Michel Rouvaen

**Abstract:**

**Keywords:**
Speech Recognition,
General Regression NeuralNetwork,
Hidden Markov Model,
Recurrent Neural Network,
ArabicDigits.

##### 4901 General Regression Neural Network and Back Propagation Neural Network Modeling for Predicting Radial Overcut in EDM: A Comparative Study

**Authors:**
Raja Das,
M. K. Pradhan

**Abstract:**

This paper presents a comparative study between two neural network models namely General Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to estimate radial overcut produced during Electrical Discharge Machining (EDM). Four input parameters have been employed: discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and discharge voltage (V). Recently, artificial intelligence techniques, as it is emerged as an effective tool that could be used to replace time consuming procedures in various scientific or engineering applications, explicitly in prediction and estimation of the complex and nonlinear process. The both networks are trained, and the prediction results are tested with the unseen validation set of the experiment and analysed. It is found that the performance of both the networks are found to be in good agreement with average percentage error less than 11% and the correlation coefficient obtained for the validation data set for GRNN and BPNN is more than 91%. However, it is much faster to train GRNN network than a BPNN and GRNN is often more accurate than BPNN. GRNN requires more memory space to store the model, GRNN features fast learning that does not require an iterative procedure, and highly parallel structure. GRNN networks are slower than multilayer perceptron networks at classifying new cases.

**Keywords:**
Electrical-discharge machining,
General Regression Neural Network,
Back-propagation Neural Network,
Radial Overcut.

##### 4900 Comparison of Neural Network and Logistic Regression Methods to Predict Xerostomia after Radiotherapy

**Authors:**
Hui-Min Ting,
Tsair-Fwu Lee,
Ming-Yuan Cho,
Pei-Ju Chao,
Chun-Ming Chang,
Long-Chang Chen,
Fu-Min Fang

**Abstract:**

To evaluate the ability to predict xerostomia after radiotherapy, we constructed and compared neural network and logistic regression models. In this study, 61 patients who completed a questionnaire about their quality of life (QoL) before and after a full course of radiation therapy were included. Based on this questionnaire, some statistical data about the condition of the patients’ salivary glands were obtained, and these subjects were included as the inputs of the neural network and logistic regression models in order to predict the probability of xerostomia. Seven variables were then selected from the statistical data according to Cramer’s V and point-biserial correlation values and were trained by each model to obtain the respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65 for SSE, and 13.7% and 19.0% for MAPE, respectively. These parameters demonstrate that both neural network and logistic regression methods are effective for predicting conditions of parotid glands.

**Keywords:**
NPC,
ANN,
logistic regression,
xerostomia.

##### 4899 Detecting Earnings Management via Statistical and Neural Network Techniques

**Authors:**
Mohammad Namazi,
Mohammad Sadeghzadeh Maharluie

**Abstract:**

**Keywords:**
Earnings management,
generalized regression neural
networks,
linear regression,
multi-layer perceptron,
Tehran stock
exchange.

##### 4898 Application of Artificial Neural Network for the Prediction of Pressure Distribution of a Plunging Airfoil

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

**Abstract:**

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

##### 4897 Artificial Neural Network based Modeling of Evaporation Losses in Reservoirs

**Authors:**
Surinder Deswal,
Mahesh Pal

**Abstract:**

**Keywords:**
Artificial neural network,
evaporation losses,
multiple linear regression,
modeling.

##### 4896 Comparison of Artificial Neural Network and Multivariate Regression Methods in Prediction of Soil Cation Exchange Capacity

**Authors:**
Ali Keshavarzi,
Fereydoon Sarmadian

**Abstract:**

**Keywords:**
Easily measurable characteristics,
Feed-forwardback propagation,
Pedotransfer functions,
CEC.

##### 4895 Dry Relaxation Shrinkage Prediction of Bordeaux Fiber Using a Feed Forward Neural

**Authors:**
Baeza S. Roberto

**Abstract:**

**Keywords:**
Neural network,
dry relaxation,
knitting,
linear
regression.

##### 4894 Sociological Impact on Education An Analytical Approach Through Artificial Neural network

**Authors:**
P. R. Jayathilaka,
K.L. Jayaratne,
H.L. Premaratne

**Abstract:**

**Keywords:**
Education,
Fuzzy,
neural network,
prediction,
Sociology

##### 4893 Influential Parameters in Estimating Soil Properties from Cone Penetrating Test: An Artificial Neural Network Study

**Authors:**
Ahmed G. Mahgoub,
Dahlia H. Hafez,
Mostafa A. Abu Kiefa

**Abstract:**

The Cone Penetration Test (CPT) is a common in-situ test which generally investigates a much greater volume of soil more quickly than possible from sampling and laboratory tests. Therefore, it has the potential to realize both cost savings and assessment of soil properties rapidly and continuously. The principle objective of this paper is to demonstrate the feasibility and efficiency of using artificial neural networks (ANNs) to predict the soil angle of internal friction (Φ) and the soil modulus of elasticity (E) from CPT results considering the uncertainties and non-linearities of the soil. In addition, ANNs are used to study the influence of different parameters and recommend which parameters should be included as input parameters to improve the prediction. Neural networks discover relationships in the input data sets through the iterative presentation of the data and intrinsic mapping characteristics of neural topologies. General Regression Neural Network (GRNN) is one of the powerful neural network architectures which is utilized in this study. A large amount of field and experimental data including CPT results, plate load tests, direct shear box, grain size distribution and calculated data of overburden pressure was obtained from a large project in the United Arab Emirates. This data was used for the training and the validation of the neural network. A comparison was made between the obtained results from the ANN's approach, and some common traditional correlations that predict Φ and E from CPT results with respect to the actual results of the collected data. The results show that the ANN is a very powerful tool. Very good agreement was obtained between estimated results from ANN and actual measured results with comparison to other correlations available in the literature. The study recommends some easily available parameters that should be included in the estimation of the soil properties to improve the prediction models. It is shown that the use of friction ration in the estimation of Φ and the use of fines content in the estimation of E considerable improve the prediction models.

**Keywords:**
Angle of internal friction,
Cone penetrating test,
General regression neural network,
Soil modulus of elasticity.

##### 4892 New Approach for Load Modeling

**Authors:**
S. Chokri

**Abstract:**

Load modeling is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

**Keywords:**
Neural network,
Load Forecasting,
Fuzzy inference,
Machine learning,
Fuzzy modeling and rule extraction,
Support
Vector Regression.

##### 4891 Performance Evaluation of Complex Valued Neural Networks Using Various Error Functions

**Authors:**
Anita S. Gangal,
P. K. Kalra,
D. S. Chauhan

**Abstract:**

**Keywords:**
Complex backpropagation algorithm,
complex errorfunctions,
complex valued neural network,
split activation function.

##### 4890 System Identification with General Dynamic Neural Networks and Network Pruning

**Authors:**
Christian Endisch,
Christoph Hackl,
Dierk Schröder

**Abstract:**

**Keywords:**
System identification,
dynamic neural network,
recurrentneural network,
GDNN,
optimization,
Levenberg Marquardt,
realtime recurrent learning,
network pruning,
quasi-online learning.

##### 4889 Time Series Forecasting Using a Hybrid RBF Neural Network and AR Model Based On Binomial Smoothing

**Authors:**
Fengxia Zheng,
Shouming Zhong

**Abstract:**

ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression (AR) model based on binomial smoothing (BS) technique which is efficient in data processing, which is called BSRBFAR. This method is examined by using the data of Canadian Lynx data. Empirical results indicate that the over-fitting problem can be eased using RBF neural network based on binomial smoothing which is called BS-RBF, and the hybrid model–BS-RBFAR can be an effective way to improve forecasting accuracy achieved by BSRBF used separately.

**Keywords:**
Binomial smoothing (BS),
hybrid,
Canadian Lynx data,
forecasting accuracy.

##### 4888 A Literature Survey of Neural Network Applications for Shunt Active Power Filters

**Authors:**
S. Janpong,
K-L. Areerak,
K-N. Areerak

**Abstract:**

**Keywords:**
Active power filter,
neural network,
harmonic
distortion,
harmonic detection and compensation,
non-linear load.

##### 4887 On Improving Breast Cancer Prediction Using GRNN-CP

**Authors:**
Kefaya Qaddoum

**Abstract:**

The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

**Keywords:**
Neural network,
conformal prediction,
cancer classification,
regression.

##### 4886 Performance Evaluation of a Neural Network based General Purpose Space Vector Modulator

**Authors:**
A.Muthuramalingam,
S.Himavathi

**Abstract:**

**Keywords:**
NN based SVM,
FPGA Implementation,
LayerMultiplexing,
NN structure and Resource Reduction,
PerformanceEvaluation

##### 4885 Extended Least Squares LS–SVM

**Authors:**
József Valyon,
Gábor Horváth

**Abstract:**

**Keywords:**
Function estimation,
Least–Squares Support VectorMachines,
Regression,
System Modeling

##### 4884 Applications of Cascade Correlation Neural Networks for Cipher System Identification

**Authors:**
B. Chandra,
P. Paul Varghese

**Abstract:**

Crypto System Identification is one of the challenging tasks in Crypt analysis. The paper discusses the possibility of employing Neural Networks for identification of Cipher Systems from cipher texts. Cascade Correlation Neural Network and Back Propagation Network have been employed for identification of Cipher Systems. Very large collection of cipher texts were generated using a Block Cipher (Enhanced RC6) and a Stream Cipher (SEAL). Promising results were obtained in terms of accuracy using both the Neural Network models but it was observed that the Cascade Correlation Neural Network Model performed better compared to Back Propagation Network.

**Keywords:**
Back Propagation Neural Networks,
CascadeCorrelation Neural Network,
Crypto systems,
Block Cipher,
StreamCipher.

##### 4883 Earth Station Neural Network Control Methodology and Simulation

**Authors:**
Hanaa T. El-Madany,
Faten H. Fahmy,
Ninet M. A. El-Rahman,
Hassen T. Dorrah

**Abstract:**

Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematical modeling of satellite earth station power system which is required for simulating the system.Aswan is selected to be the site under consideration because it is a rich region with solar energy. The complete power system is simulated using MATLAB–SIMULINK.An artificial neural network (ANN) based model has been developed for the optimum operation of earth station power system. An ANN is trained using a back propagation with Levenberg–Marquardt algorithm. The best validation performance is obtained for minimum mean square error. The regression between the network output and the corresponding target is equal to 96% which means a high accuracy. Neural network controller architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the satellite earth station power system.

**Keywords:**
Satellite,
neural network,
MATLAB,
power system.

##### 4882 Support Vector Fuzzy Based Neural Networks For Exchange Rate Modeling

**Authors:**
Prof. Chokri SLIM

**Abstract:**

A Novel fuzzy neural network combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVBFNN) is proposed. The SVBFNN combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data spaces and the efficient human-like reasoning of FNN.

**Keywords:**
Neural network,
fuzzy inference,
machine learning,
fuzzy modeling and rule extraction,
support vector regression.

##### 4881 Spline Basis Neural Network Algorithm for Numerical Integration

**Authors:**
Lina Yan,
Jingjing Di,
Ke Wang

**Abstract:**

A new basis function neural network algorithm is proposed for numerical integration. The main idea is to construct neural network model based on spline basis functions, which is used to approximate the integrand by training neural network weights. The convergence theorem of the neural network algorithm, the theorem for numerical integration and one corollary are presented and proved. The numerical examples, compared with other methods, show that the algorithm is effective and has the characteristics such as high precision and the integrand not required known. Thus, the algorithm presented in this paper can be widely applied in many engineering fields.

**Keywords:**
Numerical integration,
Spline basis function,
Neural
network algorithm

##### 4880 Developing Pedotransfer Functions for Estimating Some Soil Properties using Artificial Neural Network and Multivariate Regression Approaches

**Authors:**
Fereydoon Sarmadian,
Ali Keshavarzi

**Abstract:**

Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.

**Keywords:**
Artificial neural network,
Field capacity,
Permanentwilting point,
Pedotransfer functions.

##### 4879 Using Combination of Optimized Recurrent Neural Network with Design of Experiments and Regression for Control Chart Forecasting

**Authors:**
R. Behmanesh,
I. Rahimi

**Abstract:**

**Keywords:**
RNN,
DOE,
regression,
control chart.

##### 4878 Arterial Stiffness Detection Depending on Neural Network Classification of the Multi- Input Parameters

**Authors:**
Firas Salih,
Luban Hameed,
Afaf Kamil,
Armin Bolz

**Abstract:**

**Keywords:**
Arterial stiffness,
area under the catacrotic phase of the photoplethysmograph pulse,
neural network

##### 4877 Investigation of Artificial Neural Networks Performance to Predict Net Heating Value of Crude Oil by Its Properties

**Authors:**
Mousavian,
M. Moghimi Mofrad,
M. H. Vakili,
D. Ashouri,
R. Alizadeh

**Abstract:**

The aim of this research is to use artificial neural networks computing technology for estimating the net heating value (NHV) of crude oil by its Properties. The approach is based on training the neural network simulator uses back-propagation as the learning algorithm for a predefined range of analytically generated well test response. The network with 8 neurons in one hidden layer was selected and prediction of this network has been good agreement with experimental data.

**Keywords:**
Neural Network,
Net Heating Value,
Crude Oil,
Experimental,
Modeling.

##### 4876 Application of the Neural Network to the Synthesis of Multibeam Antennas Arrays

**Authors:**
Ridha Ghayoula,
Mbarek Traii,
Ali Gharsallah

**Abstract:**

**Keywords:**
Multibeam,
modelling,
neural networks,
synthesis,
antennas.

##### 4875 Avoiding Catastrophic Forgetting by a Dual-Network Memory Model Using a Chaotic Neural Network

**Authors:**
Motonobu Hattori

**Abstract:**

In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of chaotic associative memory and the other is a multilayer neural network. We consider that these networks correspond to the hippocampus and the neocortex of the brain, respectively. Information given is firstly stored in the hippocampal network with fast learning algorithm. Then the stored information is recalled by chaotic behavior of each neuron in the hippocampal network. Finally, it is consolidated in the neocortical network by using pseudopatterns. Computer simulation results show that the proposed model has much better ability to avoid catastrophic forgetting in comparison with conventional models.

**Keywords:**
catastrophic forgetting,
chaotic neural network,
complementary learning systems,
dual-network

##### 4874 Optimum Neural Network Architecture for Precipitation Prediction of Myanmar

**Authors:**
Khaing Win Mar,
Thinn Thu Naing

**Abstract:**

Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.

**Keywords:**
Precipitation prediction,
monthly precipitation,
neural network models,
Myanmar.