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Time-varying two-phase optimization for neural network learning

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2 Author(s)
Hyeon Myeong ; Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Taejon, South Korea ; Jong-Hwan Kim

A two-phase neural network solves exact feasible solutions when the problem is a constrained optimization programming. The time-varying programming neural network is a kind of modified steepest-gradient algorithm which solves time-varying optimization problems. In this paper, a time-varying two-phase optimization neural network is proposed which uses the merits of the two-phase neural network and the time-varying neural network. The training of multilayer neural networks is regarded as a time-varying optimization problem, and the proposed algorithm is applied to system identification using a multilayer neural network. Furthermore, we considered the case where the weights have some constraints in the learning of the neural network

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994