**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**2357

# Search results for: annealed Hopfield neural networks.

##### 2357 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

##### 2356 Globally Exponential Stability for Hopfield Neural Networks with Delays and Impulsive Perturbations

**Authors:**
Adnene Arbi,
Chaouki Aouiti,
Abderrahmane Touati

**Abstract:**

In this paper, we consider the global exponential stability of the equilibrium point of Hopfield neural networks with delays and impulsive perturbation. Some new exponential stability criteria of the system are derived by using the Lyapunov functional method and the linear matrix inequality approach for estimating the upper bound of the derivative of Lyapunov functional. Finally, we illustrate two numerical examples showing the effectiveness of our theoretical results.

**Keywords:**
Hopfield Neural Networks,
Exponential stability.

##### 2355 A New Sufficient Conditions of Stability for Discrete Time Non-autonomous Delayed Hopfield Neural Networks

**Authors:**
Adnene Arbi,
Chaouki Aouiti,
Abderrahmane Touati

**Abstract:**

In this paper, we consider the uniform asymptotic stability, global asymptotic stability and global exponential stability of the equilibrium point of discrete Hopfield neural networks with delays. Some new stability criteria for system are derived by using the Lyapunov functional method and the linear matrix inequality approach, for estimating the upper bound of Lyapunov functional derivative.

**Keywords:**
Hopfield neural networks,
uniform asymptotic stability,
global asymptotic stability,
exponential stability.

##### 2354 Blind Image Deconvolution by Neural Recursive Function Approximation

**Authors:**
Jiann-Ming Wu,
Hsiao-Chang Chen,
Chun-Chang Wu,
Pei-Hsun Hsu

**Abstract:**

This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.

**Keywords:**
Blind image deconvolution,
linear shift-invariant(LSI),
linear image degradation model,
radial basis functions (rbf),
recursive function,
annealed Hopfield neural networks.

##### 2353 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.

##### 2352 Exponential Stability of Uncertain Takagi-Sugeno Fuzzy Hopfield Neural Networks with Time Delays

**Abstract:**

In this paper, based on linear matrix inequality (LMI), by using Lyapunov functional theory, the exponential stability criterion is obtained for a class of uncertain Takagi-Sugeno fuzzy Hopfield neural networks (TSFHNNs) with time delays. Here we choose a generalized Lyapunov functional and introduce a parameterized model transformation with free weighting matrices to it, these techniques lead to generalized and less conservative stability condition that guarantee the wide stability region. Finally, an example is given to illustrate our results by using MATLAB LMI toolbox.

**Keywords:**
Hopfield neural network,
linear matrix inequality,
exponential stability,
time delay,
T-S fuzzy model.

##### 2351 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

##### 2350 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.

##### 2349 Implementation of an Associative Memory Using a Restricted Hopfield Network

**Authors:**
Tet H. Yeap

**Abstract:**

**Keywords:**
Associative memory,
Hopfield network,
Lyapunov
function,
Restricted Hopfield network.

##### 2348 The Multi-Layered Perceptrons Neural Networks for the Prediction of Daily Solar Radiation

**Authors:**
Radouane Iqdour,
Abdelouhab Zeroual

**Abstract:**

**Keywords:**
Daily solar radiation,
Prediction,
MLP neural
networks,
linear model

##### 2347 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.

##### 2346 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

##### 2345 Application of Wavelet Neural Networks in Optimization of Skeletal Buildings under Frequency Constraints

**Authors:**
Mohammad Reza Ghasemi,
Amin Ghorbani

**Abstract:**

**Keywords:**
Weight Minimization,
Frequency Constraints,
Steel
Frames,
ANN,
WNN,
RASP Function.

##### 2344 Diagnosis of Ovarian Cancer with Proteomic Patterns in Serum using Independent Component Analysis and Neural Networks

**Authors:**
Simone C. F. Neves,
Lúcio F. A. Campos,
Ewaldo Santana,
Ginalber L. O. Serra,
Allan K. Barros

**Abstract:**

**Keywords:**
Cancer ovarian,
Proteomic patterns in serum,
independent component analysis and neural networks.

##### 2343 Analysis of Periodic Solution of Delay Fuzzy BAM Neural Networks

**Authors:**
Qianhong Zhang,
Lihui Yang,
Daixi Liao

**Abstract:**

**Keywords:**
Fuzzy BAM neural networks,
Periodic solution,
Global exponential stability,
Time-varying delays

##### 2342 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

##### 2341 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.

##### 2340 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.

##### 2339 Neural Network Ensemble-based Solar Power Generation Short-Term Forecasting

**Authors:**
A. Chaouachi,
R.M. Kamel,
R. Ichikawa,
H. Hayashi,
K. Nagasaka

**Abstract:**

**Keywords:**
Neural network ensemble,
Solar power generation,
24 hour forecasting,
Comparative study

##### 2338 Investigation of Some Technical Indexes inStock Forecasting Using Neural Networks

**Authors:**
Myungsook Klassen

**Abstract:**

**Keywords:**
Stock Market Prediction,
Neural Networks,
Levenberg-Marquadt Algorithm,
Technical Indexes

##### 2337 Using Artificial Neural Networks for Optical Imaging of Fluorescent Biomarkers

**Authors:**
K. A. Laptinskiy,
S. A. Burikov,
A. M. Vervald,
S. A. Dolenko,
T. A. Dolenko

**Abstract:**

The article presents the results of the application of artificial neural networks to separate the fluorescent contribution of nanodiamonds used as biomarkers, adsorbents and carriers of drugs in biomedicine, from a fluorescent background of own biological fluorophores. The principal possibility of solving this problem is shown. Use of neural network architecture let to detect fluorescence of nanodiamonds against the background autofluorescence of egg white with high accuracy - better than 3 ug/ml.

**Keywords:**
Artificial neural networks,
fluorescence,
data
aggregation.

##### 2336 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.

##### 2335 Novel Approach for Promoting the Generalization Ability of Neural Networks

**Authors:**
Naiqin Feng,
Fang Wang,
Yuhui Qiu

**Abstract:**

**Keywords:**
Fuzzy theory,
generalization,
misclassification rate,
neural network.

##### 2334 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.

##### 2333 Application of Artificial Neural Networks for Temperature Forecasting

**Authors:**
Mohsen Hayati,
Zahra Mohebi

**Abstract:**

**Keywords:**
Artificial neural networks,
Forecasting,
Weather,
Multi-layer perceptron.

##### 2332 Exponential Stability Analysis for Switched Cellular Neural Networks with Time-varying Delays and Impulsive Effects

**Authors:**
Zixin Liu,
Fangwei Chen

**Abstract:**

In this Letter, a class of impulsive switched cellular neural networks with time-varying delays is investigated. At the same time, parametric uncertainties assumed to be norm bounded are considered. By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions guaranteeing exponential stability for all admissible parametric uncertainties are derived via constructing appropriate Lyapunov functional. One numerical example is provided to illustrate the validity of the main results obtained in this paper.

**Keywords:**
Switched systems,
exponential stability,
cellular neural networks.

##### 2331 A Novel Fuzzy-Neural Based Medical Diagnosis System

**Authors:**
S. Moein,
S. A. Monadjemi,
P. Moallem

**Abstract:**

**Keywords:**
Artificial Neural Networks,
Fuzzy Logic,
MedicalDiagnosis,
Symptoms,
Fuzzification.

##### 2330 Prediction of Bath Temperature Using Neural Networks

**Authors:**
H. Meradi,
S. Bouhouche,
M. Lahreche

**Abstract:**

In this work, we consider an application of neural networks in LD converter. Application of this approach assumes a reliable prediction of steel temperature and reduces a reblow ratio in steel work. It has been applied a conventional model to charge calculation, the obtained results by this technique are not always good, this is due to the process complexity. Difficulties are mainly generated by the noisy measurement and the process non linearities. Artificial Neural Networks (ANNs) have become a powerful tool for these complex applications. It is used a backpropagation algorithm to learn the neural nets. (ANNs) is used to predict the steel bath temperature in oxygen converter process for the end condition. This model has 11 inputs process variables and one output. The model was tested in steel work, the obtained results by neural approach are better than the conventional model.

**Keywords:**
LD converter,
bath temperature,
neural networks.

##### 2329 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.

##### 2328 Testing the Accuracy of ML-ANN for Harmonic Estimation in Balanced Industrial Distribution Power System

**Authors:**
Wael M. El-Mamlouk,
Metwally A. El-Sharkawy,
Hossam. E. Mostafa

**Abstract:**

**Keywords:**
Harmonics,
Neural Networks,
Modeling,
Simulation,
Active filters,
electric Networks.