Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
Neuro-fuzzy adaptive control based on dynamic inversion for robotic manipulators
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximation Techniques
Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
IEEE Transactions on Neural Networks
Model-reference adaptive control based on neurofuzzy networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Self-organizing neuro-fuzzy system for control of unknown plants
IEEE Transactions on Fuzzy Systems
A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
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In this paper, a high-order neuro-fuzzy network (HONFN) with improved approximation capability w.r.t. the standard high-order neural network (HONN) is proposed. In order to reduce the overall approximation error, a decomposition of the neural network (NN) approximation space into overlapping sub-regions is created and different NN approximations for each sub-region are considered. To this end, the HONFN implements a fuzzy switching among different HONNs as its input vector switches along the different sub-regions of the approximation space. The HONFN is then used to design an adaptive controller for a class of uncertain single-input single-output nonlinear systems. The proposed scheme ensures the semiglobal uniform ultimate boundedness of the tracking error within a neighborhood of the origin and the boundedness of the NN weights and control law. Furthermore, a minimal HONFN, with two properly selected fuzzy rules, guarantees that the resulting ultimate bound does not depend on the unknown optimal approximation error (as is the case for classical adaptive NN control schemes) but solely from constants chosen by the designer. A simulation study is carried out to compare the proposed scheme with a classical HONN controller.