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Abstract and Applied Analysis
Volume 2014, Article ID 178313, 9 pages
http://dx.doi.org/10.1155/2014/178313
Research Article

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

1School of Information and Communication Engineering, North University of China, Taiyuan, Shanxi 030051, China
2Department of Mathematics, North University of China, Taiyuan, Shanxi 030051, China

Received 17 April 2014; Revised 8 July 2014; Accepted 9 July 2014; Published 17 July 2014

Academic Editor: Suohai Fan

Copyright © 2014 Jinna Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.