Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.isatra.2006.08.004
Title: An on-line modified least-mean-square algorithm for training neurofuzzy controllers
Authors: Tan, W.W. 
Keywords: Adaptive neurofuzzy control
On-line learning
Issue Date: Apr-2007
Source: 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
URI: http://scholarbank.nus.edu.sg/handle/10635/55048
ISSN: 00190578
DOI: 10.1016/j.isatra.2006.08.004
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