the improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. detailed examination of changes in expression levels of genes can help physicians to have effi cient diagnosing, classification of tumors and cancer’s types as well as effective treatments. finding genes that can classify the group of...

Neural networks are widely used as classifiers in many pattern recognition problems because of good generalization abilities, what is a crucial issue in any practical application. However, vast majority of neural network architectures demands a huge computational effort for the training process, what in turn limits such solutions from application in one important domain of pattern recognition, ...

This paper serves as a tutorial on the use of neural networks for solving combinatorial optimization problems. It reviews the two main classes of neural network models: the gradientbased neural networks such as the Hopfield network, and the deformable template approaches such as the elastic net method and self-organizing maps. In each class, the original model is presented, its limitations disc...

It has been over a decade since neural networks were first applied to solve combinatorial optimization problems. During this period, enthusiasm has been erratic as new approaches are developed and (sometimes years later) their limitations are realized. This article briefly summarizes the work that has been done and presents the current standing of neural networks for combinatorial optimization ...

| The paper introduces a new approach to analyze the stability of neu-ral network models without using any Lyapunov function. With the new method, we investigate the stability properties of the general gradient-based neural network model for optimization problems. Our discussion includes both isolated equilibrium points and connected equilibrium sets which could be unbounded. For a general opti...

this article addresses an efficient and novel method for singularity-free path planning and obstacle avoidance of parallel manipulator based on neural networks. a modified 4-5-6-7 interpolating polynomial is used to plan a trajectory for a spherical parallel manipulator. the polynomial function which is smooth and continuous in displacement, velocity, acceleration and jerk is used to find a pat...

Neural networks have been proposed as a model of computation for solving a wide variety of problems in elds as diverse as combinatorial optimization, vision, and pattern recognition. The ability to map and solve a number of interesting problems on neural networks motivates a proposal for using neural networks as a highly parallel model for general-purpose computing. We review this proposal , an...

A new neural network based optimization algorithm is proposed.The presentedmodel is a discrete-time, continuous-stateHopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos andHopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their searc...