Title :
Performance of feasible Markov chain-based predictors for nonlinear systems
Author :
Gao, H. ; Karny, M. ; Slama, M.
Author_Institution :
Dept. of Adaptive Syst., Inst. of Inf. Theor. & Autom., Prague, Czech Republic
Abstract :
A non-traditional adaptive predictor is successfully compared with a neural network. It combines several simple Markov chain-based predictors gained from Bayesian estimation with forgetting. It can describe non-linear. stochastic digitized dynamic systems with finite memory and slowly varying parameters. Its complexity is linear in the number of used models m and the number of input levels mu. and quadratic in the number of output levels my. This is in sharp contrast with the corresponding full Markov predictor whose complexity is (mymu)m+1.
Keywords :
Bayes methods; Markov processes; adaptive control; computational complexity; neurocontrollers; nonlinear control systems; predictive control; stochastic systems; Bayesian estimation; Markov chain-based predictor; Markov predictor; finite memory; linear complexity; neural network; nonlinear system; nontraditional adaptive predictor; stochastic digitized dynamic system; varying parameter; Adaptation models; Approximation methods; Artificial neural networks; Bayes methods; Data models; Prediction algorithms; Predictive models; Adaptive; Estimation; Neural nets;
Conference_Titel :
Control Conference (ECC), 1997 European
Conference_Location :
Brussels
Print_ISBN :
978-3-9524269-0-6
