Performance enhancement for neural fuzzy systems using asymmetric membership functions

DOI10.1016/j.fss.2008.09.007zbMath1175.93125OpenAlexW2136431924MaRDI QIDQ1037917

Hung-Yi Pan, Ching-Hung Lee

Publication date: 17 November 2009

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2008.09.007

zbMATH Keywords

predictionnonlinear systemsapproximationmembership functioninterval-valued fuzzy setsasymmetricfuzzy logic systems

Mathematics Subject Classification ID

Fuzzy control/observation systems (93C42) Nonlinear systems in control theory (93C10)

Related Items (6)

An incremental support vector machine-trained TS-type fuzzy system for online classification problems ⋮ Interval type-2 recurrent fuzzy neural system for nonlinear systems control using stable simultaneous perturbation stochastic approximation algorithm ⋮ Interval type-2 fuzzy neural networks with asymmetric MFs based on the twice optimization algorithm for nonlinear system identification ⋮ An interval type-2 fuzzy system with a species-based hybrid algorithm for nonlinear system control design ⋮ A bilateral exchange model: the paradox of quantifying the linguistic values of qualitative characteristics ⋮ A species-based improved electromagnetism-like mechanism algorithm for TSK-type interval-valued neural fuzzy system optimization

Uses Software

• ANFIS

Cites Work

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• Interval valued fuzzy sets based on normal forms
• Interval-valued fuzzy controller based on verbal model of object
• The concept of a linguistic variable and its application to approximate reasoning. I
• Construction theorems for intuitionistic fuzzy sets
• Vague sets are intuitionistic fuzzy sets
• Indicator of inclusion grade for interval-valued fuzzy sets. Application to approximate reasoning based on interval-valued fuzzy sets
• A model reference control structure using a fuzzy neural network
• Correlation of interval-valued intuitionistic fuzzy sets
• Separation results for the stabilization of nonlinear systems using different high-gain observer designs
• An asymmetry-similarity-measure-based neural fuzzy inference system

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