A new neuro-fuzzy identification model of nonlinear dynamic systems

DOI10.1016/0888-613X(94)90007-8zbMath0794.68132OpenAlexW1976608231MaRDI QIDQ1317381

Soo-Young Lee, Cheol Hoon Park, Min-Ho Lee

Publication date: 17 April 1994

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0888-613x(94)90007-8

zbMATH Keywords

fuzzy logicgeneralizationfuzzificationmultilayer neural networksback-propagation learning algorithmsdefuzzification errorneuro-fuzzy identifier

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Fuzzy sets and logic (in connection with information, communication, or circuits theory) (94D05)

Related Items (8)

Semi-parametric estimation and forecasting for exogenous log-GARCH models ⋮ Neurofuzzy controller design using neurofuzzy identifier ⋮ Estimating GARCH models: when to use what? ⋮ Restricted normal mixture QMLE for non-stationary TGARCH(1,1) models ⋮ Finite Sample Theory of QMLE in ARCH Models with Dynamics in the Mean Equation ⋮ Estimation and Testing Stationarity for Double-Autoregressive Models ⋮ Estimation and strict stationarity testing of ARCH processes based on weighted least squares ⋮ Linear‐representation Based Estimation of Stochastic Volatility Models

Cites Work

• Membership function as an evaluation
• Numerical and applicational aspects of fuzzy relational equations
• Fuzzy logic in control systems: fuzzy logic controller. I
• Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

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