A BP Neural Network Based on Improved Particle Swarm Optimization and its Application in Reliability Forecasting

Heqing Li,Qing Tan

doi:10.19026/rjaset.6.3939

Abstract

The basic Particle Swarm Optimization (PSO) algorithm and its principle have been introduced, the Particle Swarm Optimization has low accelerate speed and can be easy to fall into local extreme value, so the Particle Swarm Optimization based on the improved inertia weight is presented. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO. To allow full play to the approximation capability of the function of BP neural network and overcome the main shortcomings of its liability to fall into local extreme value and the study proposed a concept of applying improved PSO algorithm and BP network jointly to optimize the original weight and threshold value of network and incorporating the improved PSO algorithm into BP network to establish a improved PSO-BP network system. This method improves convergence speed and the ability to search optimal value. We apply the improved particle swarm algorithm to reliability prediction. Compared with the traditional BP method, this kind of algorithm can minimize errors and improve convergence speed at the same time.

Highlights

The Particle Swarm Optimization (PSO), proposed by Kennedy and Eberhart (1995), Eberhart and Kennedy (1995) and Eberhart and Shi (2001), was based on the optimal algorithm of swarm intelligence and It guides optimal search through swarm intelligence producing by the corporation and competition among particles
For balancing between the global search and the local search and improving the precision of the result, this study proposed the nonlinear strategies for decreasing inertia weight based on the idea of the existing linear decreasing inertia weight
After being trained for 1000 times, the relative error of 38 training samples are showed by Fig. 3 we can conclude from Fig. 3 that, the relative error of samples trained by improved PSO-BP Neural Network is in the range of less 2%

Summary

INTRODUCTION

The Particle Swarm Optimization (PSO), proposed by Kennedy and Eberhart (1995), Eberhart and Kennedy (1995) and Eberhart and Shi (2001), was based on the optimal algorithm of swarm intelligence and It guides optimal search through swarm intelligence producing by the corporation and competition among particles. The optimal algorithm of improved particle swarm is not dependent of fields of problems It uses the code of decision variable as operation object and adaption function as searching objects. The optimal algorithm of improved particle swarm: With the increasing number of dimension of problems, basic PSO algorithm is falling into partial extreme value, influence the optimal function of algorithm. Every sample in the training set needs the following processing: Step 3: According to the size of every connection weight, the data of input layer are weighted and input into the activation function of hidden layer and new values are obtained. Step 3: Using the improved particle swarm algorithm to optimize the weight and the threshold value of BP network. (a) Training relative error curve of BP neural network (b) Training relative error curve of IPSO-BP neural network Fig. 3: Training relative error curve

RESULT

CONCLUSION