Stable On-Line Evolutionary Learning on NN-MLP  

Oleh:

Zhao, Q.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 6 (1997), page 1371-1378.
Topik: LEARNING; stable; evolutionary learning; NN - MLP

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

To design the nearest - neighbor - based multilayer perceptron (NN - MLP) efficiently, the author has proposed a nongenetic - based evolutionary algorithm called the R4 - rule. For off - line learning, the R4 - rule can produce the smallest or nearly smallest networks with high generalization ability by iteratively performing four basic operations : recognition, remembrance, reduction, add review. This algorithm, however, cannot be applied directly to online learning because its inherent instability, which is caused by over - reduction and over - review. To stabilize the R4 - rule, this paper proposes some improvements for reduction and review. The improved reduction is more robust for online learning because the fitness of each hidden neuron is defined by its overall behaviour in many learning cycles. The new review is more efficient because hidden neurons are adjusted in a more careful way. The performance of the improved R 4 - rule for online learning is shown by experimental results.

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