Please use this identifier to cite or link to this item:
|Title:||An on-line modified least-mean-square algorithm for training neurofuzzy controllers|
|Keywords:||Adaptive neurofuzzy control|
|Citation:||Tan, W.W. (2007-04). An on-line modified least-mean-square algorithm for training neurofuzzy controllers. ISA Transactions 46 (2) : 181-188. ScholarBank@NUS Repository. https://doi.org/10.1016/j.isatra.2006.08.004|
|Abstract:||The problem hindering the use of data-driven modelling methods for training controllers on-line is the lack of control over the amount by which the plant is excited. As the operating schedule determines the information available on-line, the knowledge of the process may degrade if the setpoint remains constant for an extended period. This paper proposes an identification algorithm that alleviates "learning interference" by incorporating fuzzy theory into the normalized least-mean-square update rule. The ability of the proposed methodology to achieve faster learning is examined by employing the algorithm to train a neurofuzzy feedforward controller for controlling a liquid level process. Since the proposed identification strategy has similarities with the normalized least-mean-square update rule and the recursive least-square estimator, the on-line learning rates of these algorithms are also compared. © 2007 ISA.|
|Source Title:||ISA Transactions|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 18, 2018
WEB OF SCIENCETM
checked on Oct 23, 2018
checked on Jun 8, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.