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Design and application of interval type-2 fuzzy neural network systems optimized with hybrid algorithms

INFORMATION SCIENCES 2025年 689卷

作者： Chen, Yang Liaoning Univ Technol Coll Sci Jinzhou 121000 Peoples R China

Interval type-2 fuzzy neural network systems (IT2 FNNSs) own the advantages of IT2 fuzzy logic systems (FLSs) and NN. The paper designs a kind of IT2 FNNSs for permanent magnetic drive (PMD) forecasting. For each rules of IT2 FNNSs, whose antecedents, consequents and input measurements are chosen as Gaussian IT2 membership functions (MFs). The proposed hybrid backpropagation (BP) algorithms and recursive least square (RLS) algorithms are adopted to tune all the parameters simultaneously. Simulation instances on the basis of data of permanent magnetic drive (PMD) torque and revolutions per minute (rpm) are adopted for testing the performances of proposed hybrid optimized IT2 FNNSs for forecasting. Convergence analysis illustrates that the IT2 FNNSs have excellent generalization capability compared with both singleton and non-singleton T1 FNNSs.

关键词： Type-2 fuzzy neural network systems Interval type-2 membership functions Back propagation algorithms recursive least square algorithms Convergence analysis

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Design and application of fuzzy neural network systems optimized with hybrid algorithms

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年第15期46卷 3063-3070页

作者： Chen, Yang Liu, Jiahuan Zhou, Junge Liaoning Univ Technol Coll Sci Jinzhou 121000 Peoples R China

Fuzzy neural network systems (FNNSs) can incorporate the merits of fuzzy logic systems (FLSs) and neural networks (NN). This paper designs a type of non-singleton FNNSs for forecasting issues. The proposed hybrid backpropagation (BP) algorithms and recursive least square (RLS) algorithms are used for optimizing the parameters of antecedents, input measurements, and consequents simultaneously. Two computer simulation examples based on the data of European Network on Intelligent Technology (EUNITE) and data of west Texas intermediate (WTI) crude oil price are used for testing. Convergence analysis shows that the hybrid optimized FNNSs have very high generalization ability.

关键词： Fuzzy neural network systems backpropagation algorithms recursive least square algorithms Gaussian type membership function generalization ability

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Design of Takagi Sugeno Kang Type Interval Type-2 Fuzzy Logic Systems Optimized with Hybrid algorithms

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2023年第2期25卷 868-879页

作者： Chen, Yang Yang, Jiaxiu Li, Chenxi Liaoning Univ Technol Coll Sci Jinzhou 121001 Peoples R China

The paper designs a type of Takagi-Sugeno-Kang (TSK) type interval type-2 fuzzy logic systems for permanent magnetic drive (PMD) coercivity and maximum energy product (MEP) forecasting. The antecedents and input measurements of interval type-2 fuzzy logic systems (IT2 FLSs) are selected as Gaussian IT2 membership function (MFs) with uncertain standard deviations. The back propagation (BP) algorithms are adopted for tuning the parameters of antecedent and input measurement. Meanwhile, the recursive least square (RLS) algorithms are adopted for tuning the parameters of consequent. Monte Carlo computer simulation examples are provided to illustrate the effective of hybrid optimized IT2 FLSs in contrast to two types of type-1 (T1) FLSs.

关键词： Interval type-2 fuzzy logic systems recursive least square algorithms Back propagation algorithms Convergence Monte Carlo simulation

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