On The Convergence of Validity Interval Analysis  

Oleh:

Maire, F.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 3 (2000), page 802-807.
Topik: interval analysis; convergence; validity interval; analysis

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Validity interval analysis (VIA) is a generic tool for analyzing the input - output behaviour of feedforward neural networks. VIA is a rule extraction technique that relies on a rule refinement algorithm. The rules are of the form Ri ? R0 i. e. "if the input of the neural network is in the region Ri, then its output is in the region R0," where regions are axis parallel hypercubes. VIA conjectures, then refines and checks rules for inconsistency. This process can be computationally expensive, and the rule refinement phase becomes critical. Hence, the importance of knowing the complexity of these rule refinement algorithms. In this paper, we show that the rule refinement part of VIA always converges in one run for single - weight - layer networks, and has an exponential average rate of convergence for multilayer networks. We also discuss some variations of the standard VIA formulae.

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