The nature of statistical learning theory
The nature of statistical learning theory
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
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Applied Optimization with MATLAB Programming
Applied Optimization with MATLAB Programming
Double inverted pendulum control based on support vector machines and fuzzy inference
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Tuning of a neuro-fuzzy controller by genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Input selection for nonlinear regression models
IEEE Transactions on Fuzzy Systems
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems
IEEE Transactions on Fuzzy Systems
Stable adaptive fuzzy control of nonlinear systems
IEEE Transactions on Fuzzy Systems
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
A versatile software tool making best use of sparse data for closed loop process control
Advances in Engineering Software
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In this work, a novel neuro-fuzzy control structure has been proposed for unknown nonlinear plants, which is referred to as the SVM-based ANFIS controller since it has been emerged from the fusion of adaptive network fuzzy inference system (ANFIS) and support vector machines (SVMs). In the proposed controller, an obtained SVM model of the plant is used to extract the gradient information and to predict the future behavior of the plant dynamics, which are necessary to find the additive correction term and to update the ANFIS parameters. The motivation behind the use of SVMs for modeling the plant dynamics is the fact that the SVM algorithms possess higher generalization ability and guarantee the global minima. The simulation results have revealed that the SVM-based ANFIS controller exhibits considerably high performance by yielding very small transient- and steady-state tracking errors and that it can maintain its performance under noisy conditions.