Blog
About

3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      An efficient self-organizing RBF neural network for water quality prediction.

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This paper presents a flexible structure Radial Basis Function (RBF) neural network (FS-RBFNN) and its application to water quality prediction. The FS-RBFNN can vary its structure dynamically in order to maintain the prediction accuracy. The hidden neurons in the RBF neural network can be added or removed online based on the neuron activity and mutual information (MI), to achieve the appropriate network complexity and maintain overall computational efficiency. The convergence of the algorithm is analyzed in both the dynamic process phase and the phase following the modification of the structure. The proposed FS-RBFNN has been tested and compared to other algorithms by applying it to the problem of identifying a nonlinear dynamic system. Experimental results show that the FS-RBFNN can be used to design an RBF structure which has fewer hidden neurons; the training time is also much faster. The algorithm is applied for predicting water quality in the wastewater treatment process. The results demonstrate its effectiveness.

          Related collections

          Author and article information

          Affiliations
          [1 ] College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China.
          Journal
          Neural Netw
          Neural networks : the official journal of the International Neural Network Society
          Elsevier BV
          1879-2782
          0893-6080
          Sep 2011
          : 24
          : 7
          S0893-6080(11)00139-0
          10.1016/j.neunet.2011.04.006
          21612889

          Comments

          Comment on this article