Dynamical Pattern Classification of Lorenz System and Chen System
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Confidence estimation of GMDH neural networks and its application in fault detection systems
International Journal of Systems Science
Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics
Mathematics and Computers in Simulation
FPGA-based real-time implementation of an adaptive RCMAC control system
WSEAS Transactions on Circuits and Systems
Direct adaptive neural control for affine nonlinear systems
Applied Soft Computing
Brief paper: Novel adaptive neural control design for nonlinear MIMO time-delay systems
Automatica (Journal of IFAC)
Adaptive CMAC neural control of chaotic systems with a PI-type learning algorithm
Expert Systems with Applications: An International Journal
A novel adaptive NN control for a class of strict-feedback nonlinear systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
A simple adaptive fuzzy control for a class of strict-feedback SISO systems
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Fuzzy-approximation-based adaptive control of strict- feedback nonlinear systems with time delays
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Adaptive dynamic RBF neural controller design for a class of nonlinear systems
Applied Soft Computing
Performance of deterministic learning in noisy environments
Neurocomputing
Human gait recognition via deterministic learning
Neural Networks
Adaptive control for nonlinear MIMO time-delay systems based on fuzzy approximation
Information Sciences: an International Journal
International Journal of Artificial Life Research
Stability analysis on pattern-based NN control systems
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.