Exponential \(\mathcal H_{\infty}\) stable learning method for Takagi-Sugeno fuzzy delayed neural networks: a convex optimization approach

DOI10.1016/j.camwa.2011.11.054zbMath1247.93011OpenAlexW2029998328MaRDI QIDQ453804

Choon Ki Ahn

Publication date: 30 September 2012

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2011.11.054

zbMATH Keywords

linear matrix inequality (LMI)exponential \(H_{\infty }\) stabilityLyapunov-Krasovskiĭ approachTakagi-Sugeno fuzzy delayed neural networksweight learning method

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Learning and adaptive systems in artificial intelligence (68T05) Fuzzy control/observation systems (93C42) (H^infty)-control (93B36) Asymptotic stability in control theory (93D20) Fuzzy functional-differential equations (34K36)

Related Items (9)

Global exponential \(p\)-stability of stochastic non-autonomous Takagi-Sugeno fuzzy cellular neural networks with time-varying delays and impulses ⋮ LMI approach to exponential stability and almost sure exponential stability for stochastic fuzzy Markovian-jumping Cohen-Grossberg neural networks with nonlinear \(p\)-Laplace diffusion ⋮ Peak-to-peak exponential direct learning of continuous-time recurrent neural network models: a matrix inequality approach ⋮ Analytical properties of credibilistic expectation functions ⋮ Passive and exponential filter design for fuzzy neural networks ⋮ Receding horizon disturbance attenuation for Takagi-Sugeno fuzzy switched dynamic neural networks ⋮ Robust delay-dependent \(H_\infty\) control for uncertain switched time-delay systems via sampled-data state feedback input ⋮ State estimation for T–S fuzzy Hopfield neural networks via strict output passivation of the error system ⋮ Stabilization of uncertain fuzzy control systems via a new descriptor system approach

Uses Software

• LMI toolbox

Cites Work

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