An improved learning algorithm for rule refinement in neuro-fuzzy modeling
Chen‐Sen Ouyang, Shie-Jue Lee · 2003
We propose an improved learning algorithm for rule refinement in neuro-fuzzy modeling. This algorithm is mainly based on a well-known technique, i.e., singular value decomposition (SVD). By using the method of SVD, the learning algorithm can converge quickly. Besides, the reasoning operator adopted in our algorithm is a compensatory fuzzy operator which has the advantage of being more adaptive and effective. Experimental results show that the proposed algorithm converges quickly and the obtained fuzzy rules are more precise.