Learning Polynomial Feedforward Neural Networks By Genetic Programming and Backpropagation  

Oleh:

Iba, H. ; Nikolaev, N. Y.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 2 (2003), page 337-350.
Topik: GENETICS; polynomials; learning polynomial; neural networks; genetic programming; backpropagation

Ketersediaan

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

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Isi artikel

This paper presents an approach to learning polynomial feedforward neural networks (PFNN s). The approach suggests, first, finding the polynomial network structure by means of a population - based search technique relying on the genetic programming paradigm, and second, further adjustment of the best discovered network weights by an especially derived backpropagation algorithm for higher order networks with polynomial activation functions. These two stages of the PFNN learning process enable us to identify networks with good training as well as generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.

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