Abstract
Normal fusion procedure of neural networks and fuzzy systems is to use neural learning techniques to train membership functions of fuzzy system. If no mechanistic prior knowledge can be used, fuzzy systems should be obtained from a set of data. The same data are usually used to train these fuzzy systems in the framework of fuzzy neural networks. But modeling accuracy cannot be improved extraordinary, because neural training and fuzzy modeling use the same data set. In this paper, we propose a new modeling idea, the fuzzy system will not be changed, and modeling error between real plant and the fuzzy system is compensated by a neural network. Another contribution of this paper is fuzzy model is generated automatically by kernel smoothing technique. The third contribution of this paper is a new learning approach for neural compensator is proposed, which assures stable and faster learning.
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Israel, C.V., Yu, W. (2010). Nonlinear System Modeling with a New Fuzzy Model and Neural Compensation. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_14
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DOI: https://doi.org/10.1007/978-3-642-12990-2_14
Publisher Name: Springer, Berlin, Heidelberg
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