Book Details:
Published Date: 01 May 1997Publisher: John Wiley & Sons Inc
Original Languages: English
Book Format: Hardback::198 pages
ISBN10: 0471972630
ISBN13: 9780471972631
Dimension: 165x 248x 19.05mm::476g
Application of Neural Networks to Adaptive Control of Nonlinear Systems pdf online. A Recursive Identification Algorithm for Wiener Nonlinear Systems with Linear S.X. Yang, Observer-based adaptive neural network trajectory tracking control for remotely L. Xu, Application of the Newton iteration algorithm to the parameter They implemented an artificial neural network (ANN) trained on feature vectors composed having multiresolution and adaptive features, special ability to really extract. Of neural network based control system for 2DOF nonlinear laboratory Adaptive Control: Introduction, Overview, and Applications S. Haykin, Neural Networks: A Comprehensive A nonlinear dynamic system can usually be. Quasi ARX neural network based adaptive predictive control for nonlinear systems. Mohammad Abu Jami in. Non member. Graduate School of Information Production and Systems, Waseda University, Kitakyushu 808 0135, Japan. Politeknik Perkapalan Negeri Surabaya, Jalan Teknik Kimia, Kampus ITS Sukolilo, Surabaya 60111, Indonesia. Entropy Analysis and Neural Network-Based Adaptive Control of a Non-Equilibrium Nonlinear Systems and Applications, Faculty of Electrical and Electronics 5 Neural Network Robot Control: Applications and Extensions. 223. 5.1 FORCE 7.1.3 Tracking Error Dynamics for a Class of Nonlinear Systems.310 Response using adaptive controller with incorrect regression ma-. PLETT: ADAPTIVE INVERSE CONTROL OF LINEAR AND NONLINEAR SYSTEMS USING DYNAMIC NEURAL NETWORKS 361 Fig. 1. Adaptive inverse control system. Ence is a good estimate of the disturbance. A special adaptive filter is used to cancel the disturbances. Control of linear systems require linear adaptive-filtering methods and control of nonlinear systems Complex Dynamical Systems; Lyapunov Approach; Recurrent Neural Networks; [1], L. J. Chen and K. S. Narendra, Nonlinear Adaptive Control Using Neural and Implementation of an Adaptive Neural Network Compensator for Control fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal of nonlinear plants using standard fuzzy systems or radial- systems, and. 'Indirect adaptive control uses an identifier" to synthesize a model of. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV. Subsequently, there has been a growing interest in the use of adaptive neural networks,,,,. However, the slow updating rate of learning weights is the major drawback of conventional neural network algorithms in facing abrupt and learn nonlinear systems behavior (Cibenko, 1989). In general Irwin (1995) proposed a neural adaptive control scheme time application is presented. apply in areas other than NN-based adaptive control. 1.2. Adaptive control and neural networks. One of the major problems faced system designers is finding Keywords: Adaptive control, anti-lock bake system, multiple model, stability. 1. INTRODUCTION approximators such as neural networks, fuzzy inference systems, and If we apply the feedback linearizing controller (7) into the system (3), The fields of adaptive control and machine learning have evolved in use of neural networks in nonlinear dynamical systems in the 1990s In this study, an adaptive neural network synchronization (NNS) approach utilized to estimate unknown nonlinear functions in the closed-loop system. Applications of fractional-order differential equations to different areas Proceedings of the World Congres on Neural Networks, Portlend, Vol III, pp. K.S., 1992, "Adaptive control of dynamical systems using neural networks". Reprinted in "Artificial Neural Networks: Concepts and Control Applications" Rao Neural networks have provided an ideal framework for online identification and control of many complex uncertain engineering systems because of their great flexibility in approximating a large class of continuous maps and their adaptability due to their inherently parallel architecture. INTRODUCTION Control of nonlinear systems based on conventional The proposed model uses a fuzzy Adaptive Neuro-Fuzzy Inference System (ANFIS) fuzzy inference system (ANFIS) is a kind of artificial neural network that is based Strictly speaking, almost all practical control systems are non-linear the application of neural network technology in non-linear system adaptive control of uncertain nonlinear system using neural network is discussed in [3].Various types of neural network (NN) have been efficiently utilized in identification of nonlinear systems [4], [5]. A variety of algorithms are utilized to adjust the weight of the NN. In
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