Please use this identifier to cite or link to this item:
|Title:||Fuzzy neural network-based adaptive single neuron controller|
|Citation:||Jia, L.,Tao, P.,Chiu, M. (2007). Fuzzy neural network-based adaptive single neuron controller. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4688 LNCS : 412-423. ScholarBank@NUS Repository.|
|Abstract:||To circumvent the drawbacks in nonlinear controller designing of chemical processes, an adaptive single neuron control scheme is proposed in this paper. A class of nonlinear processes is approximated by a fuzzy neural network-based model. The key of this work is, an adaptive single neuron controller, which mimics PID controller, is considered in the proposed control scheme. Applying this result and Lyapunov stability theory, a novel-updating algorithm to adjust the parameters of the single neuron controller is presented. Simulation results illustrate the effectiveness of the proposed adaptive single neuron control scheme. © Springer-Verlag Berlin Heidelberg 2007.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 7, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.