Othman, Mohd. Fauzi and Yusof, Mohd. Suhaimi (2008) Systems identification of dynamic system using recurrent neuro fuzzy. In: Special topics on mechatronic system design and application. Penerbit UTM, Johor, pp. 1-20. ISBN 978-983-52-0674-0
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Official URL: http://www.penerbit.utm.my/bookchapterdoc/FKE/book...
Accurate process modeling and supervision are required when dealing with advance process control. Two categories of modeling are first principles models and empirical models. To develop first principles models, one should have a good knowledge and solid understanding about the process. Although they are reliable, it is usually time consuming and effort demanding, especially for complex processes. To overcome this difficulty, empirical models based upon process input output data can be developed. The process of developing a mathematical model of a dynamic system based on the input and output data from the actual process is called identification. Nowadays, Neural Networks and Fuzzy Logic have greatly enhanced the tools to develop this process.
|Item Type:||Book Section|
|Uncontrolled Keywords:||neuro fuzzy, dynamic system|
|Subjects:||T Technology > T Technology (General)|
T Technology > TK Electrical engineering. Electronics Nuclear engineering
|Deposited By:||Fazli Masari|
|Deposited On:||02 Aug 2012 00:37|
|Last Modified:||05 Feb 2017 01:43|
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