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RMIL/AG: a new class of nonlinear conjugate gradient for training back propagation algorithm

Muhammad Basri, Sri Mazura and Mohd Nawi, Nazri and Mamat, Mustafa and Abdul Hamid , Norhamreeza (2018) RMIL/AG: a new class of nonlinear conjugate gradient for training back propagation algorithm. In: Proceedings of the Third International Conference on Soft Computing and Data Mining (SCDM 2018), 06-07 February 2018, Johor, Malaysia.

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Abstract

The conventional back propagation (BP) algorithm is generally known for some disadvantages, such as slow training, easy to getting trapped into local minima and being sensitive to the initial weights and bias. This paper introduced a new class of efficient second order conjugate gradient (CG) for training BP called Rivaie, Mustafa, Ismail and Leong (RMIL)/AG. The RMIL uses the value of adaptive gain parameter in the activation function to modify the gradient based search direction. The efficiency of the proposed method is verified by means of simulation on four classification problems. The results show that the computational efficiency of the proposed method was better than the conventional BP algorithm.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Second order method; back-propagation; conjugate gradient; search direction; classification
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 29 Aug 2019 03:46
Last Modified: 29 Aug 2019 03:46
URI: http://eprints.uthm.edu.my/id/eprint/11506
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