NDLI: Groundwater Table Prediction Based on Improved PSO Algorithm and RBF Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Qu Jihong Zhou Juan Chen Nanxiang
Copyright Year	2010
Abstract	Groundwater table often shows complex nonlinear characteristic. Radial basis function (RBF) neural network is increasingly used to predict groundwater table. The traditional RBF training algorithm based on gradient descent optimization method can only obtain the partial/local optimums solution sometimes. Furthermore, man-made selecting the structure of RBF neural network has blindness and expends much time. In training RBF neural network, particle swarm optimization (PSO) algorithm is presented to optimize and automatically determine the parameters and structure of RBF neural network. In order to improve traditional PSO algorithm searching capacity, linear inertia weight and chaos variation operator are presented. Study case shows that, compared with back propagation (BP) or RBF neural network, the new prediction model based on PSO and RBF neural network can greatly increase the convergence speed and precision.
Starting Page	228
Ending Page	232
File Size	404598
Page Count	5
File Format	PDF
ISBN	9781424484324
DOI	10.1109/AICI.2010.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Chaos Radial basis function Chaos variation operator Improved particle swarm optimization algorithm Artificial neural networks Groundwater table prediction model Predictive models Prediction algorithms Approximation algorithms Data models Linear inertia weight
Content Type	Text
Resource Type	Article

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