Improvement Of Hopfield Neural Networks And Its Applications In Wireless Commutations Optimization

In recent years, Intelligent Optimization Algorithms has become a mature intelligent optimization algorithm, more and more researchers to join in this field. Hopfield neural networks as a typical intelligent optimization algorithms has been rapid developed because its unique advantage. Now it has been an all aspects relatively complete intelligent optimization technology.This paper introduces the theory of Hopfield neural networks briefly, aim at the shortage in the using of the Hopfield neural networks we proposed many improved algorithms, and then give some applications in the field of communication.The main contributions of this dissertation are as follows:This paper mainly proposed three improved Hopfield neural networks algorithms, which are dynamic step Hopfield neural networks、simulated annealing Hopfield neural networks and dynamic step chaotic Hopfield neural networks. They are effectively in high speed calculating for optimization problems especially for Non-Polynomial problems, and these improved algorithms not only increase the rate of convergence but also improve the veracity of the optimal solutions. This paper also use the improved HNN to solve traveling salesman problem、optimization spectrum using problem in cognitive radio and prolonging the lifetime of the wireless sensor networks.We solve the optimization power allocation problem based on cognitive radio network system. We propose a Hybrid Spectrum Access (HSA) method which considers the total transmit power constraint, the peak power constraint and the primary users’tolerance. In order to solve this combinational optimization problem and achieve the global optimal solution, we derived a Simulated Annealing-Hopfield neural networks (SA-HNN). The simulation results of the optimized ergodic capacity shows that the proposed optimization problem can be solved more efficiently and better by SA-HNN than HNN or Simulated Annealing (SA), and the proposed HSA method by SA-HNN can achieve a better ergodic capacity than the traditional methods.In wireless sensor networks (WSNs), making use of the energy efficiently is becoming increasingly important, we improves the well known cluster-based LEACH(Low-Energy Adaptive Clustering Hierarchy) protocol by defining a new cost function, which aims to minimize the intra-cluster distance and the energy consumption of the network. Moreover, this paper aims at the local optimization and the convergent rate problem of the Hopfield neural networks(HNN), and proposes an improved HNN which is based on dynamic step and tent map chaotic search algorithm and we test the performance of this network by using the TSP. Thus, we solve the optimization protocol by the improved HNN. Our protocol is compared with the traditional LEACH, simulation results demonstrate that the proposed protocol can achieve longer network lifetime.In the end we summarize the characteristics of the application of the algorithm and give future research trends of the Hopfield neural networks.