SVD Reduction in Continuos Environment Reinforcement Learning
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
On the use of fuzzy inference techniques in assessment models: part I--theoretical properties
Fuzzy Optimization and Decision Making
On the use of fuzzy inference techniques in assessment models: part II: industrial applications
Fuzzy Optimization and Decision Making
A new method for fuzzy rule base reduction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper introduces a new approach for fuzzy approximation of continuous function on a compact domain. The approach calls for sampling the function over a set of rectangular grid points and applying singular value decomposition to the sample matrix. The resulting quantities are then tailored to become rule consequences and membership functions via the conditions of sum normalization and non-negativeness. The inference paradigm of product-sum-gravity is apparent from the structure of the decomposition equation. All information are extracted directly from the function samples. The present approach yields a class of equivalent fuzzy approximator to a given function. A tight bounding technique to facilitate normal or close-to-normal membership functions is also formulated. The fuzzy output approximates the given function to within an error which is dependent on the sampling intervals and the singular values discarded from the approximation process. Trade-off between the number of membership functions and the desired approximation accuracy is also discussed