The effect of adaptive parameters on the performance of back propagation
The Back Propagation algorithm or its variation on Multilayered Feedforward Networks is widely used in many applications. However, this algorithm is well-known to have difficulties with local minima problem particularly caused by neuron saturation in the hidden layer. Most existing approaches mod...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 語言: | English English English |
| 出版: |
2012
|
| 主題: | |
| 在線閱讀: | http://eprints.uthm.edu.my/2344/1/24p%20NORHAMREEZA%20ABDUL%20HAMID.pdf http://eprints.uthm.edu.my/2344/2/NORHAMREEZA%20ABDUL%20HAMID%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2344/3/NORHAMREEZA%20ABDUL%20HAMID%20WATERMARK.pdf |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | The Back Propagation algorithm or its variation on Multilayered Feedforward
Networks is widely used in many applications. However, this algorithm is
well-known to have difficulties with local minima problem particularly caused by
neuron saturation in the hidden layer. Most existing approaches modify the learning
model in order to add a random factor to the model, which overcomes the tendency
to sink into local minima. However, the random perturbations of the search direction
and various kinds of stochastic adjustment to the current set of weights are not
effective in enabling a network to escape from local minima which cause the network
fail to converge to a global minimum within a reasonable number of iterations. Thus,
this research proposed a new method known as Back Propagation Gradient Descent
with Adaptive Gain, Adaptive Momentum and Adaptive Learning Rate
(BPGD-AGAMAL) which modifies the existing Back Propagation Gradient Descent
algorithm by adaptively changing the gain, momentum coefficient and learning rate.
In this method, each training pattern has its own activation functions of neurons in
the hidden layer. The activation functions are adjusted by the adaptation of gain
parameters together with adaptive momentum and learning rate value during the
learning process. The efficiency of the proposed algorithm is compared with
conventional Back Propagation Gradient Descent and Back Propagation Gradient
Descent with Adaptive Gain by means of simulation on six benchmark problems
namely breast cancer, card, glass, iris, soybean, and thyroid. The results show that
the proposed algorithm extensively improves the learning process of conventional
Back Propagation algorithm. |
|---|