Title :
Generating adaptive models of dynamic systems with recurrent neural networks
Author :
Catfolis, Thierry
Author_Institution :
Dept. of Chem. Eng., Katholieke Univ., Leuven, Belgium
fDate :
27 Jun-2 Jul 1994
Abstract :
Presents a method for building adaptive neural network models based on the real-time recurrent learning (RTRL) algorithm developed by Williams and Zipser (1989). The author introduces the error injection method as adaptation principle. This method consists of feeding back the model error as input to the network what causes the model to react to it. The main advantages of this method are a higher stability, and a better and faster model compared to networks using only the RTRL algorithm as adaptation rule. The author demonstrates the properties of this technique with a mathematical example and with an example based on a bioreactor model
Keywords :
learning (artificial intelligence); modelling; recurrent neural nets; adaptive models; adaptive neural network models; bioreactor model; dynamic systems; error injection method; model error; real-time recurrent learning algorithm; recurrent neural networks; Adaptive systems; Chemical engineering; Control system synthesis; Expert systems; Mathematical model; Neural networks; Neurons; Nonlinear dynamical systems; Predictive models; Recurrent neural networks;
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
DOI :
10.1109/ICNN.1994.374754
