Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A course in fuzzy systems and control
A course in fuzzy systems and control
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
A fuzzy-neural multi-model for nonlinear systems identification and control
Fuzzy Sets and Systems
Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution
IEEE Transactions on Fuzzy Systems
A two-stage algorithm for identification of nonlinear dynamic systems
Automatica (Journal of IFAC)
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In this paper, a neurofuzzy adaptive control framework for discrete-time systems based on kernel smoothing regression is developed. Kernel regression is a nonparametric statistics technique used to determine a regression model where no model assumption has been done. Due to similarity with fuzzy systems, kernel smoothing is used to obtain knowledge about the structure of the fuzzy system and this information is used as initial conditions of the adaptive neurofuzzy control. Results of simulation shows the efficiency of this technique