Discrete-Time Convergence Theory and Updating Rules for Neural Networks With Energy Functions  

Oleh:

Wang, Lipo

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 2 (1997), page 445-447.
Topik: NEURAL NETWORKS; discrete - time; neural networks; energy functions

Ketersediaan

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

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Isi artikel

We present convergence theorems for neural networks with arbitrary energy functions and discrete - time dynamics for both discrete and continuous neuronal input - output - functions. We discuss systematically how the neuronal updating rule should be extracted once an energy function is constructed for a given application, in order to guarantee the descent and minimization of the energy function as the network updates. We explain why the existing theory may lead to inaccurate results and oscillatory behaviours in the convergence process. We also point out the reason for and the side effects of using hysteresis neurons to suppress these oscillatory behaviours.

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