**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**4285

# Search results for: complex valued neural network

##### 4285 Complex-Valued Neural Network in Signal Processing: A Study on the Effectiveness of Complex Valued Generalized Mean Neuron Model

**Authors:**
Anupama Pande,
Ashok Kumar Thakur,
Swapnoneel Roy

**Abstract:**

**Keywords:**
Complex valued neural network,
Generalized Meanneuron model,
Signal processing.

##### 4284 Complex-Valued Neural Network in Image Recognition: A Study on the Effectiveness of Radial Basis Function

**Authors:**
Anupama Pande,
Vishik Goel

**Abstract:**

A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units in a complex-valued neural network model that uses the back-propagation algorithm (called 'Complex-BP') for learning. Our experiments results demonstrate the effectiveness of a Radial basis function in a complex valued neural network in image recognition over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed.

**Keywords:**
Complex valued neural network,
Radial BasisFunction,
Image recognition.

##### 4283 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.

##### 4282 Fast Complex Valued Time Delay Neural Networks

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

**Keywords:**
Fast Complex Valued Time Delay Neural
Networks,
Cross Correlation,
Frequency Domain

##### 4281 Complex-Valued Neural Networks for Blind Equalization of Time-Varying Channels

**Authors:**
Rajoo Pandey

**Abstract:**

Most of the commonly used blind equalization algorithms are based on the minimization of a nonconvex and nonlinear cost function and a neural network gives smaller residual error as compared to a linear structure. The efficacy of complex valued feedforward neural networks for blind equalization of linear and nonlinear communication channels has been confirmed by many studies. In this paper we present two neural network models for blind equalization of time-varying channels, for M-ary QAM and PSK signals. The complex valued activation functions, suitable for these signal constellations in time-varying environment, are introduced and the learning algorithms based on the CMA cost function are derived. The improved performance of the proposed models is confirmed through computer simulations.

**Keywords:**
Blind Equalization,
Neural Networks,
Constant
Modulus Algorithm,
Time-varying channels.

##### 4280 Neural Network Imputation in Complex Survey Design

**Authors:**
Safaa R. Amer

**Abstract:**

Missing data yields many analysis challenges. In case of complex survey design, in addition to dealing with missing data, researchers need to account for the sampling design to achieve useful inferences. Methods for incorporating sampling weights in neural network imputation were investigated to account for complex survey designs. An estimate of variance to account for the imputation uncertainty as well as the sampling design using neural networks will be provided. A simulation study was conducted to compare estimation results based on complete case analysis, multiple imputation using a Markov Chain Monte Carlo, and neural network imputation. Furthermore, a public-use dataset was used as an example to illustrate neural networks imputation under a complex survey design

**Keywords:**
Complex survey,
estimate,
imputation,
neural networks,
variance.

##### 4279 A Novel Hopfield Neural Network for Perfect Calculation of Magnetic Resonance Spectroscopy

**Authors:**
Hazem M. El-Bakry

**Abstract:**

In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR signal. To calculate these parameters efficiently, a new model called modified Hopfield neural network is designed. The main achievement of this paper over the work in literature [30] is that the speed of the modified Hopfield neural network is accelerated. This is done by applying cross correlation in the frequency domain between the input values and the input weights. The modified Hopfield neural network can accomplish complex dignals perfectly with out any additinal computation steps. This is a valuable advantage as NMR signals are complex-valued. In addition, a technique called “modified sequential extension of section (MSES)" that takes into account the damping rate of the NMR signal is developed to be faster than that presented in [30]. Simulation results show that the calculation precision of the spectrum improves when MSES is used along with the neural network. Furthermore, MSES is found to reduce the local minimum problem in Hopfield neural networks. Moreover, the performance of the proposed method is evaluated and there is no effect on the performance of calculations when using the modified Hopfield neural networks.

**Keywords:**
Hopfield Neural Networks,
Cross Correlation,
Nuclear Magnetic Resonance,
Magnetic Resonance Spectroscopy,
Fast Fourier Transform.

##### 4278 Particle Swarm Optimization with Interval-valued Genotypes and Its Application to Neuroevolution

**Authors:**
Hidehiko Okada

**Abstract:**

The author proposes an extension of particle swarm optimization (PSO) for solving interval-valued optimization problems and applies the extended PSO to evolutionary training of neural networks (NNs) with interval weights. In the proposed PSO, values in the genotypes are not real numbers but intervals. Experimental results show that interval-valued NNs trained by the proposed method could well approximate hidden target functions despite the fact that no training data was explicitly provided.

**Keywords:**
Evolutionary algorithms,
swarm intelligence,
particle swarm optimization,
neural network,
interval arithmetic.

##### 4277 Facial Emotion Recognition with Convolutional Neural Network Based Architecture

**Authors:**
Koray U. Erbas

**Abstract:**

Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

**Keywords:**
Convolutional Neural Network,
Deep Learning,
Deep Learning Based FER,
Facial Emotion Recognition.

##### 4276 Comparison of Two Interval Models for Interval-Valued Differential Evolution

**Authors:**
Hidehiko Okada

**Abstract:**

The author previously proposed an extension of differential evolution. The proposed method extends the processes of DE to handle interval numbers as genotype values so that DE can be applied to interval-valued optimization problems. The interval DE can employ either of two interval models, the lower and upper model or the center and width model, for specifying genotype values. Ability of the interval DE in searching for solutions may depend on the model. In this paper, the author compares the two models to investigate which model contributes better for the interval DE to find better solutions. Application of the interval DE is evolutionary training of interval-valued neural networks. A result of preliminary study indicates that the CW model is better than the LU model: the interval DE with the CW model could evolve better neural networks.

**Keywords:**
Evolutionary algorithms,
differential evolution,
neural network,
neuroevolution,
interval arithmetic.

##### 4275 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.

##### 4274 Application of Neural Network on the Loading of Copper onto Clinoptilolite

**Authors:**
John Kabuba

**Abstract:**

The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ionexchange.

**Keywords:**
Clinoptilolite,
loading,
modeling,
Neural network.

##### 4273 Using Neural Network for Execution of Programmed Pulse Width Modulation (PPWM) Method

**Authors:**
M. Tarafdar Haque,
A. Taheri

**Abstract:**

**Keywords:**
Neural Network,
Inverter,
PPWM.

##### 4272 Modeling of Co-Cu Elution From Clinoptilolite using Neural Network

**Authors:**
John Kabuba,
Antoine Mulaba-Bafubiandi

**Abstract:**

The elution process for the removal of Co and Cu from clinoptilolite as an ion-exchanger was investigated using three parameters: bed volume, pH and contact time. The present paper study has shown quantitatively that acid concentration has a significant effect on the elution process. The favorable eluant concentration was found to be 2 M HCl and 2 M H2SO4, respectively. The multi-component equilibrium relationship in the process can be very complex, and perhaps ill-defined. In such circumstances, it is preferable to use a non-parametric technique such as Neural Network to represent such an equilibrium relationship.

**Keywords:**
Clinoptilolite,
elution,
modeling,
neural network.

##### 4271 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.

##### 4270 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.

##### 4269 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

##### 4268 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.

##### 4267 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.

##### 4266 ANN Based Model Development for Material Removal Rate in Dry Turning in Indian Context

**Authors:**
Mangesh R. Phate,
V. H. Tatwawadi

**Abstract:**

This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.

The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.

**Keywords:**
Field data based model,
Artificial neural network,
Simulation,
Convectional Turning,
Material removal rate.

##### 4265 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

##### 4264 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.

##### 4263 Approximate Bounded Knowledge Extraction Using Type-I Fuzzy Logic

**Authors:**
Syed Muhammad Aqil Burney,
Tahseen Ahmed Jilani,
C. Ardil

**Abstract:**

Using neural network we try to model the unknown function f for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain quasi-fuzzy weight sets (QFWS) using repeated simulation of the crisp neural network. Such type of fuzzy weight functions may be applied where we have multivariate crisp input that needs to be adjusted after iterative learning, like claim amount distribution analysis. As real data is subjected to noise and uncertainty, therefore, QFWS may be helpful in the simplification of such complex problems. Secondly, these QFWS provide good initial solution for training of fuzzy neural networks with reduced computational complexity.

**Keywords:**
Crisp neural networks,
fuzzy systems,
extraction of logical rules,
quasi-fuzzy numbers.

##### 4262 Some Remarkable Properties of a Hopfield Neural Network with Time Delay

**Authors:**
Kelvin Rozier,
Vladimir E. Bondarenko

**Abstract:**

**Keywords:**
Chaos,
Hopfield neural network,
noise,
synchronization

##### 4261 Neuro-Fuzzy Network Based On Extended Kalman Filtering for Financial Time Series

**Authors:**
Chokri Slim

**Abstract:**

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.

##### 4260 Development of Gas Chromatography Model: Propylene Concentration Using Neural Network

**Authors:**
Areej Babiker Idris Babiker,
Rosdiazli Ibrahim

**Abstract:**

**Keywords:**
Analyzer,
Levenberg-Marquardt,
Gas
chromatography,
Neural network

##### 4259 Performances Comparison of Neural Architectures for On-Line Speed Estimation in Sensorless IM Drives

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

**Abstract:**

**Keywords:**
Sensorless IM drives,
rotor speed estimators,
artificial neural network,
feed- forward architecture,
single neuron
cascaded architecture.

##### 4258 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.

##### 4257 Dynamic Fuzzy-Neural Network Controller for Induction Motor Drive

**Authors:**
M. Zerikat,
M. Bendjebbar,
N. Benouzza

**Abstract:**

In this paper, a novel approach for robust trajectory tracking of induction motor drive is presented. By combining variable structure systems theory with fuzzy logic concept and neural network techniques, a new algorithm is developed. Fuzzy logic was used for the adaptation of the learning algorithm to improve the robustness of learning and operating of the neural network. The developed control algorithm is robust to parameter variations and external influences. It also assures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the designed controller of induction motor drives which considered as highly non linear dynamic complex systems and variable characteristics over the operating conditions.

**Keywords:**
Induction motor,
fuzzy-logic control,
neural network control,
indirect field oriented control.

##### 4256 Identify Features and Parameters to Devise an Accurate Intrusion Detection System Using Artificial Neural Network

**Authors:**
Saman M. Abdulla,
Najla B. Al-Dabagh,
Omar Zakaria

**Abstract:**

The aim of this article is to explain how features of attacks could be extracted from the packets. It also explains how vectors could be built and then applied to the input of any analysis stage. For analyzing, the work deploys the Feedforward-Back propagation neural network to act as misuse intrusion detection system. It uses ten types if attacks as example for training and testing the neural network. It explains how the packets are analyzed to extract features. The work shows how selecting the right features, building correct vectors and how correct identification of the training methods with nodes- number in hidden layer of any neural network affecting the accuracy of system. In addition, the work shows how to get values of optimal weights and use them to initialize the Artificial Neural Network.

**Keywords:**
Artificial Neural Network,
Attack Features,
MisuseIntrusion Detection System,
Training Parameters.