Earticle

초록

영어

The back propagation (BP) algorithm is a very popular learning approach in multilayer feedforward networks. However, the most serious problems associated with the BP are local minima problem and slow convergence speeds. Over the years, many improvements and modifications of the BP learning algorithm have been reported. In this research, we propose a new modified BP learning algorithm by introducing adaptive gain together with adaptive momentum and adaptive learning rate into weight update process. By computer simulations, we demonstrate that the proposed algorithm can give a better convergence rate and can find a good solution in early time compare to the conventional BP. We use four common benchmark classification problems to illustrate the improvement in convergence time.

목차

Abstract
 1. Introduction
 2. The Effect of Gain Parameter on the Performance of Back Propagation Algorithm
 3. The Proposed Algorithm
 4. Results and Discussions
  4.1. Iris Classification Problem
  4.2. Card Classification Problem
  4.3. Glass Cassification Problem
  4.4. Thyroid Classification Problem
 5. Conclusions
 References

키워드

저자

• Norhamreeza Abdul Hamid [ Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, MALAYSIA. ]
• Nazri Mohd Nawi [ Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, MALAYSIA. ]
• Rozaida Ghazali [ Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, MALAYSIA. ]
• Mohd Najib Mohd Salleh [ Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, MALAYSIA. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605