Abstract
This paper proposes a fuzzy CMAC model with truth value restriction inference scheme, which provide the original CMAC with a firm and intuitive fuzzy logic reasoning framework. Our proposed model smoothens the network output and increases the approximation ability, as well as reduces the memory requirement. Moreover, the membership functions and the fuzzy rules used in the fuzzy CMAC have clear semantic meaning. Our experiments are conducted on some benchmark datasets, and the results show that our method outperforms the existing representative techniques. The high learning capability of our model results from the ability to handle uncertainty in the inference process.
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Shi, D., Harkisanka, A., Quek, C. (2004). CMAC with Fuzzy Logic Reasoning. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_138
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DOI: https://doi.org/10.1007/978-3-540-30499-9_138
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
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