Time-Varying Two-Phase Optimization and Its Application to Neural-Network Learning  

Oleh:

Myung, Hyun ; Kim, Jong-Hwan

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 6 (1997), page 1293-1300.
Topik: neural network; time - varying; two - phase optimization; neural - network; learning

Ketersediaan

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

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Isi artikel

In this paper, a time - varying two - phase (TVTP) optimization neural network is proposed based on the two - phase neural network and the time - varying programming neural network. The proposed TVTP algorithm gives exact feasible solutions with a finite penalty parameter when the problem is a constrained time - varying optimization. It can be applied to system identification and control where it has some constraints on weights in the learning of the neural network. To demonstrate its effectiveness and applicability, the proposed algorithm is applied to the learning of a neo - fuzzy neuron model.

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