Fuzzy inference using piecewise polynomial interpolation and its application to model-reference fuzzy logic controller
A new fuzzy inference based on piecewise polynomial interpolation similar to the spline technique is proposed. The signed membership function which can encode more information than the usual membership function is also introduced and used together with this new inference method. The fuzzy system using this inference method is more compact compared to other types of fuzzy systems. The computational load when implementing this inference method is almost comparable to the singleton method even if high-order polynomial series are used. A training algorithm similar to the back-propagation algorithm is also proposed to tune these polynomials if numerical training data is available. In contrast to neural network where the trained network is only a function of training data, here, both the heuristic prior knowledge and available training data are used. A simulation example is given to show how this new fuzzy inference can be applied in a model reference closed-loop control system. (C) 2001 Elsevier Science B.V. All rights reserved.
fuzzy inference polynomial interpolation fuzzy logic signed membership function closed-loop control