Abstract
Functional reasoning or the Takagi-Sugeno-Kang model is a fuzzy reasoning method aiming at numerical accuracy and has found wide use in fuzzy modeling. In this method, each rule consists of a fuzzy implication and a functional consequence part. In this work, a new, online identification method for such a system is presented, for supervised learning tasks. Structure identification is executed by a fuzzy ART learning module, following the procedure of splitting fuzzy rules that tend to produce high output error. Fuzzy rules are also added wherever the error exceeds a threshold. All parameters are fine tuned by the δ rule, a basic learning technique in neural networks. Computer simulation exhibits the potentials of this approach, which is tested with well known benchmarks, yielding excellent results.
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© 1998 Springer-Verlag Berlin Heidelberg
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Tzafestaş, S.G., Zikidis, K.C. (1998). A new on-line structure and parameter learning architecture for fuzzy modeling, based on neural and fuzzy techniques. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_423
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DOI: https://doi.org/10.1007/3-540-64574-8_423
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