Time-varying two-phase optimization and its application to neural-network learning

Author

Myung, Hyun ; Kim, Jong-Hwan

Author_Institution

Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea

Volume

8

Issue

6

fYear

1997

fDate

11/1/1997 12:00:00 AM

Firstpage

1293

Lastpage

1300

Abstract

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

Keywords

learning (artificial intelligence); neural nets; optimisation; TVTP algorithm; constrained time-varying optimization; control; finite penalty parameter; neo-fuzzy neuron model learning; neural-network learning; system identification; time-varying two-phase optimization; Artificial neural networks; Circuits; Constraint optimization; Control systems; Linear programming; Multi-layer neural network; Neural networks; Neurons; Switches; System identification;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.641452

Filename

641452