A Systematic Approach to a Mult-FNN for Model Optimization

S.-K. Oh, T.-C. Ahn, H.-S. Park, and H.-S. Hwang (Korea)


FNN(Fuzzy-Neural Networks), HCM(Hard C-Means)clustering method, Genetic algorithms, Fuzzy inference method, Aggregate objective function.


In this paper, the Multi-FNN (Fuzzy-Neural Networks) model is identified and optimized using HCM (Hard C Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on FNN and use simplified and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and genetic algorithms (GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time-series data for gas furnace, NOx emission process data of gas turbine power plant and the numerical data of nonlinear function.

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