With the proved efficacy on solving linear time-varying matrix or vector equations, Zhang neural network (ZNN) could be generalized and developed for the online minimization of time-varying quadratic functions. The minimum of a time-varying quadratic function can be reached exactly and rapidly by using Zhang neural network, as compared with conventional gradient-based neural networks (GNN). Com...

In order to deeply mine the information of Oil Atomic Emission Spectrum Data, a simulation model and a prediction model of Cu concentration of a type of sixcylinder diesel engine were established by applying Generalized Regression Neural Network. Seven different working conditions had been set up and sixty-nine oil samples had been taken from engine. The results show that the absolute errors of...

We generalize the 1/k-ensemble algorithm so that it can be used for both discrete and continuous systems, and show that the generalization is correct numerically and mathematically. We also compare the efficiencies of the generalized 1/k-ensemble algorithm and the generalized Wang-Landau algorithm through a neural network example. The numerical results favor to the generalized 1/k-ensemble algo...

Abstract: In this research, at first, the natural frequencies of a cracked beam are obtained analytically, then, location and depth of a crack in beam is identified by neural network method. The research is applied on a beam with an open crack for three different boundary conditions. For this purpose, at first, the natural frequencies of the cracked beam are obtained analytically, to get the ex...

the hybrid fuzzy differential equations have a wide range of applications in science and engineering. we consider the problem of nding their numerical solutions by using a novel hybrid method based on fuzzy neural network. here neural network is considered as a part of large eld called neural computing or soft computing. the proposed algorithm is illustrated by numerical examples and the resu...

in this paper, we introduce a hybrid approach based on neural network and optimization teqnique to solve ordinary differential equation. in proposed model we use heyperbolic secont transformation function in hiden layer of neural network part and bfgs teqnique in optimization part. in comparison with existing similar neural networks proposed model provides solutions with high accuracy. numerica...

Industrial pollution is often considered to be one of the prime factors contributing to air, water and soil pollution. Sectoral pollution loads (ton/yr) into different media (i.e. air, water and land) in Lagos were estimated using Industrial Pollution Projected System (IPPS). These were further studied using Artificial neural Networks (ANNs), a data mining technique that has the ability of dete...

Earthquakes are one of the most destructive natural phenomena which consist of rapid vibrations of rock near the earth’s surface. Because of their unpredictable occurrence and enormous capacity of destruction, they have brought fear to mankind since ancient times. Usually the earthquake acceleration is noted from the equipment in crisp or exact form. But in actual practice those data may not be...

Feature extraction is a crucial part of classijicationprocedures. In this paper we present an approach, how to utilize feature extraction criteria to predict the potential efficiency of a neural network classijiel: Statistical and geometrical criteria are introduced for analysis. The complete system of our research consists of a class of generalized Hough-Transformations for feature extraction ...