Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction

Jinna Lu,Hongping Hu,Yanping Bai

doi:10.1155/2014/178313

Abstract

This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW-PSO). Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW-PSO algorithm to determine the centers, widths, and connection weights of RBF neural network. To assess the performance of the proposed EDIW-PSO-RBF model, we choose the daily air quality index (AQI) of Xi’an for prediction and obtain improved results.

Highlights

The radial basis function (RBF) neural network is a novel and effective feed-forward neural network [1], which has good performance of best approximation and global optimum
We present and discuss an improved EDIWPSO-RBF model to solve prediction problem
Exponential Decreasing Inertia Weight (EDIW)-particle swarm optimizing (PSO)-RBF model is applied to the daily air quality index (AQI) prediction comparing with Linearly Decreasing Inertia Weight (LDIW)-PSO-RBF model, nonlinear decreasing inertia weight (NDIW)-PSO-RBF model, and GLbestIW-PSO-RBF model

Summary

Introduction

The radial basis function (RBF) neural network is a novel and effective feed-forward neural network [1], which has good performance of best approximation and global optimum. The RBF neural network architecture has three layers composed of input layer, hidden layer, and output layer. Cj and dj are, respectively, the center and the width of the jth hidden neuron, and cj is an I-dimension vector as cj = J=1 where ωjk is the connection weight between the jth hidden layer and the kth output of the network. RBF neural network contains three groups of parameters which are centers cji, widths dj, and connection weights ωjk. We propose an Exponential Decreasing Inertia Weight PSO (EDIW-PSO) algorithm to get the optimal parameters of RBF network.

Dynamic Particle Swarm Optimizing Algorithm

The Proposed RBF Model by EDIW-PSO Algorithm

Experiment

Conclusion