In this paper we present an improved neural network to solve strictly convex quadratic programming(QP) problem. The proposed model is derived based on a piecewise equation correspond to optimality condition of convex (QP) problem and has a lower structure complexity respect to the other existing neural network model for solving such problems. In theoretical aspect, stability and global converge...

In order to predict the ice accretion on overhead line conductors, five artificial neural network (ANN) architectures were explored and compared. Two static networks, Multilayer Perceptron and Radial Basis Functions, as well as two time dependent networks, Finite Impulse Response and Elman, were compared with multiple linear regression (ADALINE). Results indicated that the FIR network yielded t...

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...

In this work we study, analytically and employing Monte Carlo simulations, the influence of the competition between several activity-dependent synaptic processes, such as short-term synaptic facilitation and depression, on the maximum memory storage capacity in a neural network. In contrast with the case of synaptic depression, which drastically reduces the capacity of the network to store and ...

-In order to overcome the defects of the traditional static load modeling in coalmine, a new modeling method is proposed in this paper. First, a new clustering method based on improved PSO algorithm is presented to classify the load data in order to reduce the number of load model before modeling. Second, RBF neural network based on improved PSO algorithm is put forward to establish aggregate l...