Radial basis function neural network based on an improved exponential decreasing inertia weight-particle swarm optimization algorithm for AQI prediction
type: Item
instance of: scholarly article
title: Radial basis function neural network based on an improved exponential decreasing inertia weight-particle swarm optimization algorithm for AQI prediction
zbMATH Open document ID: 1470.90162
DOI: 10.1155/2014/178313
author: Jinna Lu
author: Yan-Ping Bai
author: Hongping Hu
published in: Abstract and Applied Analysis
publication date: 2019-02-14T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 7021876
Wikidata QID: Q59035791
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/178313
OpenAlex ID: W2008566064
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description: scientific article; zbMATH DE number 7021876