**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**1600

# Search results for: BP Neural Network

##### 1600 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.

##### 1599 Accelerating Integer Neural Networks On Low Cost DSPs

**Authors:**
Thomas Behan,
Zaiyi Liao,
Lian Zhao,
Chunting Yang

**Abstract:**

**Keywords:**
Digital Signal Processor (DSP),
Integer Neural Network(INN),
Low Cost Neural Network,
Integer Neural Network DSPImplementation.

##### 1598 Intelligent Earthquake Prediction System Based On Neural Network

**Authors:**
Emad Amar,
Tawfik Khattab,
Fatma Zada

**Abstract:**

Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information about Earthquake Existed throughout history & the Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of the object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network.

**Keywords:**
BP neural network,
Prediction,
RBF neural network.

##### 1597 Neural Network based Texture Analysis of Liver Tumor from Computed Tomography Images

**Authors:**
K.Mala,
V.Sadasivam,
S.Alagappan

**Abstract:**

Advances in clinical medical imaging have brought about the routine production of vast numbers of medical images that need to be analyzed. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. Computed Tomography (CT) is highly accurate for diagnosing liver tumors. This study aimed to evaluate the potential role of the wavelet and the neural network in the differential diagnosis of liver tumors in CT images. The tumors considered in this study are hepatocellular carcinoma, cholangio carcinoma, hemangeoma and hepatoadenoma. Each suspicious tumor region was automatically extracted from the CT abdominal images and the textural information obtained was used to train the Probabilistic Neural Network (PNN) to classify the tumors. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.

**Keywords:**
Fuzzy c means clustering,
texture analysis,
probabilistic neural network,
LVQ neural network.

##### 1596 Automatic Removal of Ocular Artifacts using JADE Algorithm and Neural Network

**Authors:**
V Krishnaveni,
S Jayaraman,
A Gunasekaran,
K Ramadoss

**Abstract:**

**Keywords:**
Auto Regressive (AR) Coefficients,
Feed Forward Neural Network (FNN),
Joint Approximation Diagonalisation of Eigen matrices (JADE) Algorithm,
Polynomial Neural Network (PNN).

##### 1595 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.

##### 1594 Detection of Moving Images Using Neural Network

**Authors:**
P. Latha,
L. Ganesan,
N. Ramaraj,
P. V. Hari Venkatesh

**Abstract:**

Motion detection is a basic operation in the selection of significant segments of the video signals. For an effective Human Computer Intelligent Interaction, the computer needs to recognize the motion and track the moving object. Here an efficient neural network system is proposed for motion detection from the static background. This method mainly consists of four parts like Frame Separation, Rough Motion Detection, Network Formation and Training, Object Tracking. This paper can be used to verify real time detections in such a way that it can be used in defense applications, bio-medical applications and robotics. This can also be used for obtaining detection information related to the size, location and direction of motion of moving objects for assessment purposes. The time taken for video tracking by this Neural Network is only few seconds.

**Keywords:**
Frame separation,
Correlation Network,
Neural network training,
Radial Basis Function,
object tracking,
Motion Detection.

##### 1593 On The Analysis of a Compound Neural Network for Detecting Atrio Ventricular Heart Block (AVB) in an ECG Signal

**Authors:**
Salama Meghriche,
Amer Draa,
Mohammed Boulemden

**Abstract:**

**Keywords:**
Artificial neural networks,
Electrocardiogram(ECG),
Feed forward multilayer neural network,
Medical diagnosis,
Pattern recognitionm,
Signal processing.

##### 1592 Evaluating Performance of an Anomaly Detection Module with Artificial Neural Network Implementation

**Authors:**
Edward Guillén,
Jhordany Rodriguez,
Rafael Páez

**Abstract:**

Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

**Keywords:**
Network Intrusion Detection,
Machine learning,
Artificial Neural Network.

##### 1591 STLF Based on Optimized Neural Network Using PSO

**Authors:**
H. Shayeghi,
H. A. Shayanfar,
G. Azimi

**Abstract:**

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.

**Keywords:**
Large Neural Network,
Short-Term Load Forecasting,
Particle Swarm Optimization.

##### 1590 Intelligent Neural Network Based STLF

**Authors:**
H. Shayeghi,
H. A. Shayanfar,
G. Azimi

**Abstract:**

Short-Term Load Forecasting (STLF) plays an important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural networks for load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. We find good performance for the large neural networks. The proposed methodology gives lower percent errors all the time. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.

**Keywords:**
Feed-forward Large Neural Network,
Short-TermLoad Forecasting,
Continuous Genetic Algorithm.

##### 1589 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

##### 1588 A Statistical Prediction of Likely Distress in Nigeria Banking Sector Using a Neural Network Approach

**Authors:**
D. A. Farinde

**Abstract:**

One of the most significant threats to the economy of a nation is the bankruptcy of its banks. This study evaluates the susceptibility of Nigerian banks to failure with a view to identifying ratios and financial data that are sensitive to solvency of the bank. Further, a predictive model is generated to guide all stakeholders in the industry. Thirty quoted banks that had published Annual Reports for the year preceding the consolidation i.e. year 2004 were selected. They were examined for distress using the Multilayer Perceptron Neural Network Analysis. The model was used to analyze further reforms by the Central Bank of Nigeria using published Annual Reports of twenty quoted banks for the year 2008 and 2011. The model can thus be used for future prediction of failure in the Nigerian banking system.

**Keywords:**
Bank,
Bankruptcy,
Financial Ratios,
Neural Network,
Multilayer Perceptron,
Predictive Model

##### 1587 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.

##### 1586 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.

##### 1585 Identification of Nonlinear Systems Using Radial Basis Function Neural Network

**Authors:**
C. Pislaru,
A. Shebani

**Abstract:**

This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function.

**Keywords:**
System identification,
Nonlinear system,
Neural
networks,
RBF neural network.

##### 1584 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.

##### 1583 A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients

**Authors:**
Zarita Zainuddin,
Ong Pauline,
C. Ardil

**Abstract:**

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

##### 1582 Using Neural Network for Execution of Programmed Pulse Width Modulation (PPWM) Method

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

**Abstract:**

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

##### 1581 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

##### 1580 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

##### 1579 Application of Neural Network for Contingency Ranking Based on Combination of Severity Indices

**Authors:**
S. Jadid,
S. Jalilzadeh

**Abstract:**

**Keywords:**
composite indices,
transient stability,
neural network.

##### 1578 Antenna Array Beamforming Using Neural Network

**Authors:**
Maja Sarevska,
Abdel-Badeeh M. Salem

**Abstract:**

This paper considers the problem of Null-Steering beamforming using Neural Network (NN) approach for antenna array system. Two cases are presented. First, unlike the other authors, the estimated Direction Of Arrivals (DOAs) are used for antenna array weights NN-based determination and the imprecise DOAs estimations are taken into account. Second, the blind null-steering beamforming is presented. In this case the antenna array outputs are presented at the input of the NN without DOAs estimation. The results of computer simulations will show much better relative mean error performances of the first NN approach compared to the NNbased blind beamforming.

**Keywords:**
Beamforming,
DOAs,
neural network.

##### 1577 Estimation of the Bit Side Force by Using Artificial Neural Network

**Authors:**
Mohammad Heidari

**Abstract:**

**Keywords:**
Artificial Neural Network,
BHA,
Horizontal Well,
Stabilizer.

##### 1576 Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier

**Authors:**
I. Omerhodzic,
S. Avdakovic,
A. Nuhanovic,
K. Dizdarevic

**Abstract:**

In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (δ, θ, α, β and γ) and the Parseval-s theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.

**Keywords:**
Epilepsy,
EEG,
Wavelet transform,
Energydistribution,
Neural Network,
Classification.

##### 1575 Application of Fuzzy Neural Network for Image Tumor Description

**Authors:**
Nahla Ibraheem Jabbar,
Monica Mehrotra

**Abstract:**

This paper used a fuzzy kohonen neural network for medical image segmentation. Image segmentation plays a important role in the many of medical imaging applications by automating or facilitating the diagnostic. The paper analyses the tumor by extraction of the features of (area, entropy, means and standard deviation).These measurements gives a description for a tumor.

**Keywords:**
FCM,
features extraction,
medical image processing,
neural network,
segmentation.

##### 1574 Optimizing the Probabilistic Neural Network Training Algorithm for Multi-Class Identification

**Authors:**
Abdelhadi Lotfi,
Abdelkader Benyettou

**Abstract:**

In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

**Keywords:**
Classification,
probabilistic neural networks,
network optimization,
pattern recognition.

##### 1573 Improvement of Synchronous Machine Dynamic Characteristics via Neural Network Based Controllers

**Authors:**
S. A. Gawish,
F. A. Khalifa,
R. M. Mostafa

**Abstract:**

**Keywords:**
Adaptive artificial neural network,
power system
stabilizer,
synchronous generator.

##### 1572 Binary Mixture of Copper-Cobalt Ions Uptake by Zeolite using Neural Network

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

**Abstract:**

**Keywords:**
Adsorption isotherm,
binary system,
neural network;
sorption

##### 1571 MITAutomatic ECG Beat Tachycardia Detection Using Artificial Neural Network

**Authors:**
R. Amandi,
A. Shahbazi,
A. Mohebi,
M. Bazargan,
Y. Jaberi,
P. Emadi,
A. Valizade

**Abstract:**

**Keywords:**
Fuzzy Logic,
Probabilistic Neural Network,
Tachycardia,
Wavelet Transform.