융합신호처리학회 학술대회논문집 (Proceedings of the Korea Institute of Convergence Signal Processing)
- 한국신호처리시스템학회 2000년도 추계종합학술대회논문집
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- Pages.201-204
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- 2000
신경망 학습 변수의 시변 제어에 관한 연구
A study on time-varying control of learning parameters in neural networks
초록
This paper describes a study on the time-varying control of parameters in learning of the neural network. Elman recurrent neural network (RNN) is used to implement the control of parameters. The parameters of learning and momentum rates In the error backpropagation algorithm ate updated at every iteration using fuzzy rules based on performance index. In addition, the gain and slope of the neuron's activation function are also considered time-varying parameters. These function parameters are updated using the gradient descent algorithm. Simulation results show that the auto-tuned learning algorithm results in faster convergence and lower system error than regular backpropagation in the system identification.
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