Neural Systems for Control Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. - Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory - Represents the most up-to-date developments in this rapidly growing application area of neural networks - Takes a new and novel approach to system identification and synthesis |
Contents
| 1 | |
| 7 | |
| 31 | |
a Step Toward Biological Arm Control | 61 |
Chapter 5 Neuronal Modeling of the Baroreceptor Reflex with Applications in Process Modeling and Control | 87 |
Chapter 6 Identification of Nonlinear Dynamical Systems Using Neural Networks | 129 |
Chapter 7 Neural Network Control of Robot Arms and Nonlinear Systems | 161 |
Chapter 8 Neural Networks for Intelligent Sensors and Control Practical Issues and Some Solutions | 213 |
Chapter 9 Approximation of TimeOptimal Control for an Industrial Production Plant with General Regression Neural Network | 235 |
Optimization Aspects | 259 |
Chapter 11 Reconfigurable Neural Control in Precision Space Structural Platforms | 289 |
Chapter 12 Neural Approximations for Finite and InfiniteHorizon Optimal Control | 317 |
| 353 | |
Other editions - View all
 |
Common terms and phrases
action activation adaptive control algorithm applications approach architecture artificial neural networks auto-tuner backpropagation baroreceptor baroreflex behavior bounded chapter closed-loop collinearity components computational control design control law control structure control system cortex cortical layer cortical units cost function defined denoted described discrete-time dynamic programming dynamic system Equation example feedback feedforward neural network first-order given gradient GRNN hand position hidden layer identification IEEE Transactions input input-output model learning system linear Markov matrix methods model arm motor muscle length neural control neural net neural network training neuro-control design neurons nip-section NN controller NN weights nonlinear systems observability on-line optimal control optimal control problem outliers output parameters performance plant prediction process model properties proprioceptive recognition regression reinforcement learning reinforcement signal robot robust second-order neurons Section sensors sequence setpoint space stability supervised learning theorem tion tracking error trajectory variables Viterbi algorithm
