**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**1629

# Search results for: Fast Neural Networks

##### 1629 Fast Object/Face Detection Using Neural Networks and Fast Fourier Transform

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

**Abstract:**

**Keywords:**
Conventional Neural Networks,
Fast Neural
Networks,
Cross Correlation in the Frequency Domain.

##### 1628 Massively-Parallel Bit-Serial Neural Networks for Fast Epilepsy Diagnosis: A Feasibility Study

**Authors:**
Si Mon Kueh,
Tom J. Kazmierski

**Abstract:**

**Keywords:**
Artificial Neural Networks,
bit-serial neural processor,
FPGA,
Neural Processing Element.

##### 1627 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

##### 1626 Sub-Image Detection Using Fast Neural Processors and Image Decomposition

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

**Abstract:**

In this paper, an approach to reduce the computation steps required by fast neural networksfor the searching process is presented. The principle ofdivide and conquer strategy is applied through imagedecomposition. Each image is divided into small in sizesub-images and then each one is tested separately usinga fast neural network. The operation of fast neuralnetworks based on applying cross correlation in thefrequency domain between the input image and theweights of the hidden neurons. Compared toconventional and fast neural networks, experimentalresults show that a speed up ratio is achieved whenapplying this technique to locate human facesautomatically in cluttered scenes. Furthermore, fasterface detection is obtained by using parallel processingtechniques to test the resulting sub-images at the sametime using the same number of fast neural networks. Incontrast to using only fast neural networks, the speed upratio is increased with the size of the input image whenusing fast neural networks and image decomposition.

**Keywords:**
Fast Neural Networks,
2D-FFT,
CrossCorrelation,
Image decomposition,
Parallel Processing.

##### 1625 A Modified Cross Correlation in the Frequency Domain for Fast Pattern Detection Using Neural Networks

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

**Abstract:**

**Keywords:**
Fast Pattern Detection,
Neural Networks,
Modified Cross Correlation

##### 1624 A Fast Neural Algorithm for Serial Code Detection in a Stream of Sequential Data

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

**Abstract:**

In recent years, fast neural networks for object/face detection have been introduced based on cross correlation in the frequency domain between the input matrix and the hidden weights of neural networks. In our previous papers [3,4], fast neural networks for certain code detection was introduced. It was proved in [10] that for fast neural networks to give the same correct results as conventional neural networks, both the weights of neural networks and the input matrix must be symmetric. This condition made those fast neural networks slower than conventional neural networks. Another symmetric form for the input matrix was introduced in [1-9] to speed up the operation of these fast neural networks. Here, corrections for the cross correlation equations (given in [13,15,16]) to compensate for the symmetry condition are presented. After these corrections, it is proved mathematically that the number of computation steps required for fast neural networks is less than that needed by classical neural networks. Furthermore, there is no need for converting the input data into symmetric form. Moreover, such new idea is applied to increase the speed of neural networks in case of processing complex values. Simulation results after these corrections using MATLAB confirm the theoretical computations.

**Keywords:**
Fast Code/Data Detection,
Neural Networks,
Cross Correlation,
real/complex values.

##### 1623 Fast Adjustable Threshold for Uniform Neural Network Quantization

**Authors:**
Alexander Goncharenko,
Andrey Denisov,
Sergey Alyamkin,
Evgeny Terentev

**Abstract:**

**Keywords:**
Distillation,
machine learning,
neural networks,
quantization.

##### 1622 Integrating Fast Karnough Map and Modular Neural Networks for Simplification and Realization of Complex Boolean Functions

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

**Abstract:**

In this paper a new fast simplification method is presented. Such method realizes Karnough map with large number of variables. In order to accelerate the operation of the proposed method, a new approach for fast detection of group of ones is presented. Such approach implemented in the frequency domain. The search operation relies on performing cross correlation in the frequency domain rather than time one. It is proved mathematically and practically that the number of computation steps required for the presented method is less than that needed by conventional cross correlation. Simulation results using MATLAB confirm the theoretical computations. Furthermore, a powerful solution for realization of complex functions is given. The simplified functions are implemented by using a new desigen for neural networks. Neural networks are used because they are fault tolerance and as a result they can recognize signals even with noise or distortion. This is very useful for logic functions used in data and computer communications. Moreover, the implemented functions are realized with minimum amount of components. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such approach is applied to implement XOR function, 16 logic functions on one bit level, and 2-bit digital multiplier. Compared to previous non- modular designs, a clear reduction in the order of computations and hardware requirements is achieved.

**Keywords:**
Boolean functions,
simplification,
Karnough map,
implementation of logic functions,
modular neural networks.

##### 1621 Investigation of Improved Chaotic Signal Tracking by Echo State Neural Networks and Multilayer Perceptron via Training of Extended Kalman Filter Approach

**Authors:**
Farhad Asadi,
S. Hossein Sadati

**Abstract:**

**Keywords:**
Feedforward neural networks,
nonlinear signal
prediction,
echo state neural networks approach,
leaking rates,
capacity of neural networks.

##### 1620 A New High Speed Neural Model for Fast Character Recognition Using Cross Correlation and Matrix Decomposition

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

**Abstract:**

**Keywords:**
Fast Character Detection,
Neural Processors,
Cross Correlation,
Image Normalization,
Parallel Processing.

##### 1619 Fast Forecasting of Stock Market Prices by using New High Speed Time Delay Neural Networks

**Authors:**
Hazem M. El-Bakry,
Nikos Mastorakis

**Abstract:**

**Keywords:**
Fast Forecasting,
Stock Market Prices,
Time Delay NeuralNetworks,
Cross Correlation,
Frequency Domain.

##### 1618 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.

##### 1617 Comparison between Beta Wavelets Neural Networks, RBF Neural Networks and Polynomial Approximation for 1D, 2DFunctions Approximation

**Authors:**
Wajdi Bellil,
Chokri Ben Amar,
Adel M. Alimi

**Abstract:**

This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. Our purpose is to approximate an unknown function f: Rn - R from scattered samples (xi; y = f(xi)) i=1....n, where first, we have little a priori knowledge on the unknown function f: it lives in some infinite dimensional smooth function space and second the function approximation process is performed iteratively: each new measure on the function (xi; f(xi)) is used to compute a new estimate Ôêºf as an approximation of the function f. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed Beta wavelet network.

**Keywords:**
Beta wavelets networks,
RBF neural network,
training algorithms,
MSE,
1-D,
2D function approximation.

##### 1616 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.

##### 1615 The Impact of the Number of Neurons in the Hidden Layer on the Performance of MLP Neural Network: Application to the Fast Identification of Toxic Gases

**Authors:**
Slimane Ouhmad,
Abdellah Halimi

**Abstract:**

In this work, neural networks methods MLP type were applied to a database from an array of six sensors for the detection of three toxic gases. The choice of the number of hidden layers and the weight values are influential on the convergence of the learning algorithm. We proposed, in this article, a mathematical formula to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases and optimized the computation time. The model presented here has proven to be an effective application for the fast identification of toxic gases.

**Keywords:**
Back-propagation,
Computing time,
Fast identification,
MLP neural network,
Number of neurons in the hidden layer.

##### 1614 A New Recognition Scheme for Machine- Printed Arabic Texts based on Neural Networks

**Authors:**
Z. Shaaban

**Abstract:**

This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.

**Keywords:**
Neural Networks,
character recognition,
feature
extraction,
multiple networks,
Arabic text.

##### 1613 Detecting and Secluding Route Modifiers by Neural Network Approach in Wireless Sensor Networks

**Authors:**
C. N. Vanitha,
M. Usha

**Abstract:**

In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

**Keywords:**
Neural networks,
pattern learning,
security,
wireless sensor networks.

##### 1612 Self-evolving Neural Networks Based On PSO and JPSO Algorithms

**Authors:**
Abdussamad Ismail,
Dong-Sheng Jeng

**Abstract:**

A self-evolution algorithm for optimizing neural networks using a combination of PSO and JPSO is proposed. The algorithm optimizes both the network topology and parameters simultaneously with the aim of achieving desired accuracy with less complicated networks. The performance of the proposed approach is compared with conventional back-propagation networks using several synthetic functions, with better results in the case of the former. The proposed algorithm is also implemented on slope stability problem to estimate the critical factor of safety. Based on the results obtained, the proposed self evolving network produced a better estimate of critical safety factor in comparison to conventional BPN network.

**Keywords:**
Neural networks,
Topology evolution,
Particle
swarm optimization.

##### 1611 A Model-following Adaptive Controller for Linear/Nonlinear Plantsusing Radial Basis Function Neural Networks

**Authors:**
Yuichi Masukake,
Yoshihisa Ishida

**Abstract:**

**Keywords:**
Linear/nonlinear plants,
neural networks,
radial basisfunction networks.

##### 1610 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.

##### 1609 Auto-regressive Recurrent Neural Network Approach for Electricity Load Forecasting

**Authors:**
Tarik Rashid,
B. Q. Huang,
M-T. Kechadi,
B. Gleeson

**Abstract:**

this paper presents an auto-regressive network called the Auto-Regressive Multi-Context Recurrent Neural Network (ARMCRN), which forecasts the daily peak load for two large power plant systems. The auto-regressive network is a combination of both recurrent and non-recurrent networks. Weather component variables are the key elements in forecasting because any change in these variables affects the demand of energy load. So the AR-MCRN is used to learn the relationship between past, previous, and future exogenous and endogenous variables. Experimental results show that using the change in weather components and the change that occurred in past load as inputs to the AR-MCRN, rather than the basic weather parameters and past load itself as inputs to the same network, produce higher accuracy of predicted load. Experimental results also show that using exogenous and endogenous variables as inputs is better than using only the exogenous variables as inputs to the network.

**Keywords:**
Daily peak load forecasting,
neural networks,
recurrent neural networks,
auto regressive multi-context neural network.

##### 1608 Dynamic Threshold Adjustment Approach For Neural Networks

**Authors:**
Hamza A. Ali,
Waleed A. J. Rasheed

**Abstract:**

The use of neural networks for recognition application is generally constrained by their inherent parameters inflexibility after the training phase. This means no adaptation is accommodated for input variations that have any influence on the network parameters. Attempts were made in this work to design a neural network that includes an additional mechanism that adjusts the threshold values according to the input pattern variations. The new approach is based on splitting the whole network into two subnets; main traditional net and a supportive net. The first deals with the required output of trained patterns with predefined settings, while the second tolerates output generation dynamically with tuning capability for any newly applied input. This tuning comes in the form of an adjustment to the threshold values. Two levels of supportive net were studied; one implements an extended additional layer with adjustable neuronal threshold setting mechanism, while the second implements an auxiliary net with traditional architecture performs dynamic adjustment to the threshold value of the main net that is constructed in dual-layer architecture. Experiment results and analysis of the proposed designs have given quite satisfactory conducts. The supportive layer approach achieved over 90% recognition rate, while the multiple network technique shows more effective and acceptable level of recognition. However, this is achieved at the price of network complexity and computation time. Recognition generalization may be also improved by accommodating capabilities involving all the innate structures in conjugation with Intelligence abilities with the needs of further advanced learning phases.

**Keywords:**
Classification,
Recognition,
Neural Networks,
Pattern Recognition,
Generalization.

##### 1607 Neural Networks: From Black Box towards Transparent Box Application to Evapotranspiration Modeling

**Authors:**
A. Johannet,
B. Vayssade,
D. Bertin

**Abstract:**

**Keywords:**
Neural-Networks,
Hydrology,
Evapotranpiration,
Hidden Function Modeling.

##### 1606 Exploiting Kinetic and Kinematic Data to Plot Cyclograms for Managing the Rehabilitation Process of BKAs by Applying Neural Networks

**Authors:**
L. Parisi

**Abstract:**

Kinematic data wisely correlate vector quantities in space to scalar parameters in time to assess the degree of symmetry between the intact limb and the amputated limb with respect to a normal model derived from the gait of control group participants. Furthermore, these particular data allow a doctor to preliminarily evaluate the usefulness of a certain rehabilitation therapy. Kinetic curves allow the analysis of ground reaction forces (GRFs) to assess the appropriateness of human motion. Electromyography (EMG) allows the analysis of the fundamental lower limb force contributions to quantify the level of gait asymmetry. However, the use of this technological tool is expensive and requires patient’s hospitalization. This research work suggests overcoming the above limitations by applying artificial neural networks.

**Keywords:**
Kinetics,
kinematics,
cyclograms,
neural networks.

##### 1605 Recognition of Noisy Words Using the Time Delay Neural Networks Approach

**Authors:**
Khenfer-Koummich Fatima,
Mesbahi Larbi,
Hendel Fatiha

**Abstract:**

This paper presents a recognition system for isolated words like robot commands. It’s carried out by Time Delay Neural Networks; TDNN. To teleoperate a robot for specific tasks as turn, close, etc… In industrial environment and taking into account the noise coming from the machine. The choice of TDNN is based on its generalization in terms of accuracy, in more it acts as a filter that allows the passage of certain desirable frequency characteristics of speech; the goal is to determine the parameters of this filter for making an adaptable system to the variability of speech signal and to noise especially, for this the back propagation technique was used in learning phase. The approach was applied on commands pronounced in two languages separately: The French and Arabic. The results for two test bases of 300 spoken words for each one are 87%, 97.6% in neutral environment and 77.67%, 92.67% when the white Gaussian noisy was added with a SNR of 35 dB.

**Keywords:**
Neural networks,
Noise,
Speech Recognition.

##### 1604 Voice Disorders Identification Using Hybrid Approach: Wavelet Analysis and Multilayer Neural Networks

**Authors:**
L. Salhi,
M. Talbi,
A. Cherif

**Abstract:**

**Keywords:**
Formants,
Neural Networks,
Pathological Voices,
Pitch,
Wavelet Transform.

##### 1603 A New Robust Stability Criterion for Dynamical Neural Networks with Mixed Time Delays

**Authors:**
Guang Zhou,
Shouming Zhong

**Abstract:**

In this paper, we investigate the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks, the neutral system has mixed time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we drive a new criterion for the robust stability of a class of neural networks with time delays by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Numerical examples are given to illustrate the effectiveness and the advantage of the proposed main results.

**Keywords:**
Neural networks,
Delayed systems,
Lyapunov function,
Stability analysis.

##### 1602 Integrating Fast Karnough Map and Modular Neural Networks for Simplification and Realization of Complex Boolean Functions

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

**Abstract:**

**Keywords:**
Boolean Functions,
Simplification,
KarnoughMap,
Implementation of Logic Functions,
Modular NeuralNetworks.

##### 1601 Artificial Neural Networks Application to Improve Shunt Active Power Filter

**Authors:**
Rachid.Dehini,
Abdesselam.Bassou,
Brahim.Ferdi

**Abstract:**

**Keywords:**
Artificial Neural Networks (ANN),
p-q theory,
(SAPF),
Harmonics,
Total Harmonic Distortion.

##### 1600 Comparative Analysis of Sigmoidal Feedforward Artificial Neural Networks and Radial Basis Function Networks Approach for Localization in Wireless Sensor Networks

**Authors:**
Ashish Payal,
C. S. Rai,
B. V. R. Reddy

**Abstract:**

With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m^{2} grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.

**Keywords:**
Localization,
wireless sensor networks,
artificial neural network,
radial basis function,
multi-layer perceptron,
backpropagation,
RSSI.