Structure self-organizing algorithm for fuzzy neural networks and its applications
模糊神经网络的结构自组织算法及应用

A new self-organizing algorithm for fuzzy neural networks is proposed,which automates the structure and parameter identification simultaneously based on input-target samples.Firstly,a self-organizing clustering method is used to establish the network structure and the initial values of its parameters.Then a supervised learning is applied to optimize these parameters.An example of nonlinear function approximation is given to demonstrate the effectiveness of the algo- rithm,where some comparisons are made with other approaches.Finally,based on the data of a wastewater treatment plant, a forecast model of the output-water quality is developed using the established fuzzy neural networks.Simulation results show that the output-water quality can be well predicted by the model.