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ArtikelFuzzy-Neural Network With General Parameter Adaption for Modeling of Nonlinear Time Series  
Oleh: Dote, Y. ; Ovaska, S. J. ; Akhmetov, D.F.
Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 1 (2001), page 148-152.
Topik: non linear; fuzzy - neural; network; general parameter; adaption; non linear time series
Ketersediaan
  • Perpustakaan Pusat (Semanggi)
    • Nomor Panggil: II36.5
    • Non-tandon: 1 (dapat dipinjam: 0)
    • Tandon: tidak ada
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Isi artikelBy taking advantage of fuzzy systems and neural networks, a fuzzy - neural network with a general parameter (GP) learning algorithm and heuristic model structure determination is proposed in this paper. Our network model is based on the Gaussian radial basis function network (RBFN). We use the flexible GP approach both for initializing the off - line training algorithm and fine - tuning the non linear model efficiently in online operation. A modification of the robust unbiasedness criterion using distorter (UCD) is utilized for selecting the structural parameters of this adaptive model. The UCD approach provides the desired modeling accuracy and avoids the risk of over - fitting. In order to illustrate the operation of the proposed modeling scheme, it is experimentally applied to a fault detection application.
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