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

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 al...

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Vydáno v:Abstract and Applied Analysis Ročník 2014; číslo 2014; s. 399 - 407-144
Hlavní autoři: Lu, Jinna, Bai, Yanping, Hu, Hongping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Limiteds 01.01.2014
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Wiley
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ISSN:1085-3375, 1687-0409
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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.
AbstractList 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.
Audience Academic
Author Bai, Yanping
Hu, Hongping
Lu, Jinna
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ContentType Journal Article
Copyright Copyright © 2014 Jinna Lu et al.
COPYRIGHT 2014 John Wiley & Sons, Inc.
Copyright © 2014 Jinna Lu et al. 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.
Copyright 2014 Hindawi Publishing Corporation
Copyright_xml – notice: Copyright © 2014 Jinna Lu et al.
– notice: COPYRIGHT 2014 John Wiley & Sons, Inc.
– notice: Copyright © 2014 Jinna Lu et al. 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.
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Snippet This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization...
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SubjectTerms Colleges & universities
Data processing
Functional equations
Functions
Inertia
Mathematical optimization
Mathematical research
Methods
Neural networks
Neurons
Outdoor air quality
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Title Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction
URI https://www.airitilibrary.com/Article/Detail/P20160825001-201412-201609080014-201609080014-399-407-144
https://search.emarefa.net/detail/BIM-1013421
https://dx.doi.org/10.1155/2014/178313
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