Industrial and Manufacturing Systems, Volume 4Cornelius T. Leondes 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.
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. |
Contents
Introduction | 1 |
Active Noise and Vibration Control Using a Neural | 17 |
Conclusions | 82 |
Copyright | |
15 other sections not shown
Common terms and phrases
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 coefficient 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 iterations joint LMS algorithm matrix method neural net neural network controller neural network-based noise and vibration number of nodes observer off-line on-line output layer parameters path transfer function pH process PID controller problem radial basis function RBFNN reference signal robot set-point ship shown in Fig sigmoid function signal processing simulation skeleton smart structures system condition TANN techniques tion torque training samples trajectory transfer function model values vector vibration control wear дек