Titlebook: Recent Advances in Intelligent Control Systems; Wen Yu Book 2009 Springer-Verlag London 2009 Adaptive Control.Control.Control Applications

chastise

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创作

fatty-acids

Hybrid Differential Neural Network Identifier for Partially Uncertain Hybrid SystemsConvergence analysis is realized considering the practical stability of identification error for a general class of hybrid systems. As can be seen in the numerical examples this algorithm could be easily implemented. In this sense the . strategy of the continuous subsystems could be used in the automatic control implementation.

遗传学

Wen YuSurveys the state of the art in intelligent control systems giving the researcher and practitioner a self-contained reference to the latest results.A broad ranging tutorial in the key topics in fuzzy

Bmd955

低三下四之人

Book 2009o the style of control employed: fuzzy control; neural control; fuzzy neural control; and intelligent control. The contributions illustrate the interdisciplinary antecedents of intelligent control and contrast its results with those of more traditional control methods. A variety of design examples,

渐变

mastopexy

Neural Networks Based Probability Density Function Control for Stochastic Systems by the description of several neural networks approximations to the output PDFs. To illustrate the use of neural networks in the control design, an example of grinding process control using SDC theory is included here.

悲观

An Adaptive Neuro-fuzzy Controller for Robot Navigationt can preferably “see” the surrounding environment, avoid static and moving obstacles automatically, and generate reasonable trajectories toward the target. The effectiveness and efficiency of the proposed approach are demonstrated by simulation and experiment studies.

visceral-fat

Fuzzy Control of Large Civil Structures Subjected to Natural Hazardsate feedback controllers and Kalman filters. A nonlinear multi-input multi-output (MIMO) autoregressive exogenous (ARX) Takagi-Sugeno (T-S) fuzzy model is constructed out of a set of linear dynamic models. Subsequently, multiple Lyapunovbased state feedback controllers are formulated in terms of lin