Neural Network Systems, Techniques, and Applications
Cornelius T. Leondes
ACADEMIC PressINC, 1998 - Computers - 395 pages
Industrial and Manufacturing Systems serves as an in-depth guide to major applications in this focal area of interest to the engineering community. This volume emphasizes the neural network structures used to achieve practical and effective systems, and provides numerous examples. Industrial and Manufacturing Systems is a unique and comprehensive reference to diverse application methodologies and implementations by means of neural network systems. It willbe of use to practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering.
* Quality control techniques
* Active noise and vibration control
* Chemical processing systems
* Process monitoring and diagnosis
* Robotic assembly in electronics manufacturing systems
* Smart structural systems of improved effective-ness
* Closed loop feedback control in uncertain nonlinear manufacturing systems
* Adaptive neural controllers in industrial systems
* Machine tool control systems
Emphasis is placed on neural network structures for achieving practical and effective systems, with numerous examples illustrating the text; Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering, will find this volume a unique and comprehensive reference to diverse application methodologies and implementations by means of neura network systems.
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Neural Network Systems Techniques and Applications: Advances in Theory and ...
Cornelius T. Leondes
No preview available - 1998
Active Noise and Vibration Control Using a Neural
Neural NetworkBased Feedforward Active
17 other sections not shown
acoustic active control system active noise active noise control actuators adaptive algorithm adaptive control adaptive filtering algorithm approximation artificial neural networks cancellation path transfer closed loop components control input control signal control source CSTR denotes derived digital filter disturbance dynamical systems equation error criterion error sensor error signal feedback feedforward control feedforward neural network Figure filter weights FIR filter flawed frequency hidden layer IEEE Trans implementation inverse iteration joint layer node linear controller LMS algorithm matrix method neural network controller neural network-based noise and vibration nonlinear systems number of nodes observer off-line on-line output layer parameters path transfer function pH process PID controller problem radial basis functions RBFNN reference signal response robot robust control set-point ship shown in Fig sigmoid function signal processing simulation skeleton smart structures subskeleton system condition TANN techniques tion torque training samples trajectory transfer function model values vector