By
Omid Omidvar, University of the District of Columbia
David Elliott, Institute of Systems Research, University of Maryland
Description
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.
Audience:
Researchers and practicioners in the fields of neural networks, control engineering, mechanical engineering, and electrical engineering.
