Evolution-based design of neural fuzzy networks using self-adapting genetic parameters

Alpaydin G., DÜNDAR G., Balkir S.

IEEE Transactions on Fuzzy Systems, cilt.10, sa.2, ss.211-221, 2002 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 2
  • Basım Tarihi: 2002
  • Doi Numarası: 10.1109/91.995122
  • Dergi Adı: IEEE Transactions on Fuzzy Systems
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.211-221
  • Anahtar Kelimeler: Evolution strategies, Fuzzy logic systems (FLSs), Genetic algorithms, Neural fuzzy networks, Simulated annealing
  • Boğaziçi Üniversitesi Adresli: Evet

Özet

In this paper, an evolution-based approach to design of neural fuzzy networks is presented. The proposed strategy optimizes the whole fuzzy system with minimum rule number according to given specifications, while training the network parameters. The approach relies on an optimization tool, which combines evolution strategies and simulated annealing algorithms in finding the global optimum solution. The optimization variables include membership function parameters and rule numbers which are combined with genetic parameters to create diversity in the search space due to self-adaptation. The optimization technique is independent of the topology under consideration and capable of handling any type of membership function. The algorithmic details of the optimization methodology are discussed in detail, and the generality of the approach is illustrated by different examples.