Title :
Vibration control using CMAC neural networks with optimized weight smoothing
Author :
Kraft, L.G. ; Pallotta, Jeremy
Author_Institution :
Dept. of Electr. & Comput. Eng., New Hampshire Univ., Durham, NH, USA
Abstract :
In this paper the CMAC neural network concept is applied to active vibration control. Traditional CMAC and a new CMAC with weight smoothing are compared
Keywords :
cerebellar model arithmetic computers; computerised control; neurocontrollers; optimal control; vibration control; CMAC neural networks; active vibration control; optimized weight smoothing; Associative memory; Function approximation; Laplace equations; Neural networks; Process control; Signal processing; Smoothing methods; Table lookup; Training data; Vibration control;
Conference_Titel :
American Control Conference, 1999. Proceedings of the 1999
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4990-3
DOI :
10.1109/ACC.1999.783226
