DocumentCode :
2445675
Title :
Adaptive dynamic neural network estimators
Author :
Lainiotis, D.G. ; Plataniotis, K.N.
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida Inst. of Technol., Melbourne, FL, USA
Volume :
7
fYear :
1994
fDate :
27 Jun-2 Jul 1994
Firstpage :
4736
Abstract :
The problem of state estimation for linear or nonlinear models with unknown parameters is very important in many engineering problems. In this paper the solution to the problem of adaptive estimation for unknown state variable or chaotic models through the use of adaptive dynamic neural estimators is proposed. The proposed adaptive neural estimators are developed and their advantages are discussed. Extensive computer simulations of the application or the proposed adaptive neural estimator to state estimation as well as chaotic series prediction illustrate the effectiveness of the adaptive neural solution
Keywords :
adaptive systems; chaos; neural nets; state estimation; adaptive dynamic neural estimators; adaptive estimation; chaotic models; chaotic series prediction; state estimation; Adaptive filters; Adaptive systems; Chaos; Estimation theory; Filtering algorithms; Filtering theory; Neural networks; State estimation; State-space methods; Statistical analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
Type :
conf
DOI :
10.1109/ICNN.1994.375041
Filename :
375041
Link To Document :
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