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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Veröffentlicht in:Abstract and Applied Analysis Jg. 2014; H. 2014; S. 399 - 407-144
Hauptverfasser: Lu, Jinna, Bai, Yanping, Hu, Hongping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Limiteds 01.01.2014
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Wiley
Schlagworte:
Colleges & universities
Data processing
Functional equations
Functions
Inertia
Mathematical optimization
Mathematical research
Methods
Neural networks
Neurons
Outdoor air quality
ISSN:1085-3375, 1687-0409
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:1085-3375
1687-0409
DOI:10.1155/2014/178313