Flexible Neuro-Fuzzy Systems  

Oleh:

Rutkowski, L. ; Cpalka, K.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 3 (May 2003), page 554-574.
Topik: Neuro fuzzy; flexible; neuro - fuzzy; systems

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, we derive new neuro - fuzzy structures called flexible neuro - fuzzy inference systems or FLEXNFIS. Based on the input-output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce : 1) softness to fuzzy implication operators, to aggregation of rules and to connectives of antecedents ; 2) certainty weights to aggregation of rules and to connectives of antecedents ; and 3) parameterized families of T - norms and S - norms to fuzzy implication operators, to aggregation of rules and to connectives of antecedents. Our approach introduces more flexibility to the structure and design of neuro - fuzzy systems. Through computer simulations, we show that Mamdani - type systems are more suitable to approximation problems, whereas logical - type systems may be preferred for classification problems.

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