SOLVING LOCAL MINIMA PROBLEM IN BACK PROPAGATION ALGORITHM USING ADAPTIVE GAIN, ADAPTIVE MOMENTUM AND ADAPTIVE LEARNING RATE ON CLASSIFICATION PROBLEMS
2012 ◽
Vol 09
◽
pp. 448-455
◽
Keyword(s):
This paper presents a new method to improve back propagation algorithm from getting stuck with local minima problem and slow convergence speeds which caused by neuron saturation in the hidden layer. In this proposed algorithm, each training pattern has its own activation functions of neurons in the hidden layer that 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 the conventional back propagation gradient descent and the current working back propagation gradient descent with adaptive gain by means of simulation on three benchmark problems namely iris, glass and thyroid.
2012 ◽
Vol 09
◽
pp. 432-439
◽
2010 ◽
Vol 46
(2)
◽
pp. 173-183
◽
Keyword(s):
2011 ◽
Vol 1
(2)
◽
pp. 178
◽
2017 ◽
Vol 161
(8)
◽
pp. 5-9
◽
Keyword(s):