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

Jinna Lu, Hongping Hu and Yanping Bai

Abstract and Applied Analysis, 2014, vol. 2014, 1-9

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:178313

DOI: 10.1155/2014/178313

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