A Survey on Soft Computing Based Approaches for Fuzzy Model Identification
Neety Bansal1, Parvinder Kaur2
1Neety Bansal, ECE Department, Maharishi Markandeshwar Engineering College, MMU, Mullana, Haryana, India.
2Dr. Parvinder Kaur, ECE Department, Maharishi Markandeshwar Engineering College, MMU, Mullana, Haryana, India.
Manuscript received on june 20, 2017. | Revised Manuscript received on junw 26, 2017. | Manuscript published on july 05, 2017. | PP: 9-13 | Volume-7 Issue-3, July 2017. | Retrieval Number: C3010077317/2017©BEIESP
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©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: The identification of an optimized fuzzy model is one of the key issues in the field of fuzzy system modeling. This can be formulated as a search and optimisation problem and many hard computing as well as soft computing approaches are available in the literature to solve this problem. In this paper we have made an attempt to present a survey on fuzzy model identification using some soft computing techniques like ACO, BBO, BB-BC, ABC, etc.
Keywords: Fuzzy system, Fuzzy model identification, Soft computing, Nature inspired approaches.