Constructing optimized interval type-2 TSK neuro-fuzzy systems with noise reduction property by quantum inspired BFA

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• 作者：Sharina Huang^a ; ^bAuthor Vitae ; Minghao Chen^a ; ^{chenmh130264@aliyun.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Neuro-fuzzy modelling ; Structure identification ; Quantum inspired bacterial foraging algorithm ; Recursive least squares estimator ; Interval type-2 fuzzy sets
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：173
• 期：part_P3
• 页码：1839-1850
• 全文大小：1013 K

文摘

In this paper, a modified rule generation approach with self-constructing property for neuro-fuzzy system modelling is proposed. In structure identification stage, input–output patterns are divided into the clusters and interval type-2 membership functions are generated roughly. Interval type-2 Takagi–Sugeno–Kang (TSK) neuro-fuzzy structure is fine tuned by quantum inspired bacterial foraging algorithm (QBFA) in parameter identification stage to achieve higher precision, a recursive least squares (RLS) estimator is used to update consequent parameters. Comparisons with two type-1 neuro-fuzzy systems on three nonlinear functions and chaotic Mackey-Glass time series show that the proposed systems can approximate the target with little error. Experiments are also executed involving the proposed systems for modelling flue gas denitrification efficiency of a thermal power plant. It is verified by the results that interval type-2 neuro-fuzzy structure can learn knowledge from input–output data set with the aid of QBFA and hybrid training progresses are able to improve its performance.