Title :
Neural network learning using time-varying two-phase optimization
Author :
Myeong, Hyeon ; Kim, Jong-Hwan
Author_Institution :
Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea
Abstract :
A time-varying two-phase (TVTP) optimization algorithm is proposed based on the two-phase neural network (NN) and the time-varying programming NN. The proposed algorithm is most useful when the problem is a time-varying optimization programming which may have some constraints. Thus it can be applied to the training of the NN where it has some constraints on weights. Computer simulations show that the proposed TVTP algorithm has good adaptability in online learning and is less sensitive to the learning step size than the conventional error back propagation (EBP) method
Keywords :
learning (artificial intelligence); neural nets; optimisation; neural network learning; online learning; time-varying programming neural net; time-varying two-phase optimization; two-phase neural network; Computer errors; Computer simulation; Constraint optimization; Convergence; Ear; Functional programming; Multi-layer neural network; Neural networks; Neurons;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.411107
