Abstract
We introduec a non-fuzzy inference system based on hard partition to contruct model for nonlinear process. In fuzzy modeling, the generation of fuzzy rules has the problem that the number of fuzzy rules exponentially increases. To solve this problem, the rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions. The proposed model is evaluated with the performance using the data widely used in nonlinear process.
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© 2012 Springer-Verlag Berlin Heidelberg
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Park, KJ., Lee, JM., Kim, YK. (2012). Hard Partition-Based Non-Fuzzy Inference System for Nonlinear Process. In: Kim, Th., Lee, Yh., Fang, Wc. (eds) Future Generation Information Technology. FGIT 2012. Lecture Notes in Computer Science, vol 7709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35585-1_27
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DOI: https://doi.org/10.1007/978-3-642-35585-1_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35584-4
Online ISBN: 978-3-642-35585-1
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