Title :
Design of a static neural element in an iterative learning control scheme
Author_Institution :
Dept. of Electr. Eng., Lawrence Technol. Univ., Southfield, MI, USA
Abstract :
Most often, employing a neural net in a control system implies an adaption scheme. Neural nets inherently are non-model based, requiring the adaption. This paper proposes a design process which selects values of a single layer neural net suitable to replace a control law element
Keywords :
convergence; iterative methods; learning (artificial intelligence); neurocontrollers; stability; convergence; iterative learning control; neural net; stability; static neural element; Control systems; Convergence; Delay effects; Error correction; Frequency domain analysis; Neural networks; PD control; Process design; Stability; Time factors;
Conference_Titel :
Decision and Control, 1998. Proceedings of the 37th IEEE Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
0-7803-4394-8
DOI :
10.1109/CDC.1998.760764
