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

Authors:
Hong-Gui Han;Qi-li Chen;Jun-Fei Qiao
Affiliations:
-;-;-
Venue:
Neural Networks
Year:
2011

Citing 14
Cited 8

A resource-allocating network for function interpolation

Neural Computation
A sequential learning scheme for function approximation using minimal radial basis function neural networks

Neural Computation
A new algorithm for online structure and parameter adaptation of RBF networks

Neural Networks
Adaptive RBF network for parameter estimation and stable air-fuel ratio control

Neural Networks
Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm

Neurocomputing
A new adaptive merging and growing algorithm for designing artificial neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A growing and pruning method for radial basis function networks

IEEE Transactions on Neural Networks
A hierarchical RBF online learning algorithm for real-time 3-D scanner

IEEE Transactions on Neural Networks
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm

IEEE Transactions on Neural Networks
Effects of moving the center's in an RBF network

IEEE Transactions on Neural Networks
Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation

IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation

IEEE Transactions on Neural Networks
Self-Organizing and Self-Evolving Neurons: A New Neural Network for Optimization

IEEE Transactions on Neural Networks

Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach

Automatica (Journal of IFAC)
Soft measurement modeling based on hierarchically neural network (HNN) for wastewater treatment

ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Efficient self-organizing multilayer neural network for nonlinear system modeling

Neural Networks
Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems

Information Sciences: an International Journal
Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization

Computers and Electronics in Agriculture
A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation

Neural Networks
Hierarchical extreme learning machine for feedforward neural network

Neurocomputing
A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.