A Modified Conjugate Gradient Formula for Back Propagation Neural Network Algorithm | Zendy

Abbas Al-Bayati | Zendy; Najmaddin Sulaiman | Zendy; Gulnar W. Sadiq | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A Modified Conjugate Gradient Formula for Back Propagation Neural Network Algorithm

Author(s) -

Abbas Al-Bayati,

Najmaddin Sulaiman,

Gulnar W. Sadiq

Publication year - 2009

Publication title -

journal of computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.161

H-Index - 28

eISSN - 1552-6607

pISSN - 1549-3636

DOI - 10.3844/jcssp.2009.849.856

Subject(s) - computer science , conjugate gradient method , algorithm , artificial neural network , backpropagation , conjugate , gradient method , artificial intelligence , mathematics , mathematical analysis

Problem statement: The Conjugate Gradient (CG) algorithm which usually used for solving nonlinear functions is presented and is combined with the modified Back Propagation (BP) algorithm yielding a new fast training multilayer algorithm. Approach: This study consisted of determination of new search directions by exploiting the information calculated by gradient descent as well as the previous search direction. The proposed algorithm improved the training efficiency of BP algorithm by adaptively modifying the initial search direction. Results: Performance of the proposed algorithm was demonstrated by comparing it with the Neural Network (NN) algorithm for the chosen test functions. Conclusion: The numerical results showed that number of iterations required by the proposed algorithm to converge was less than the both standard CG and NN algorithms. The proposed algorithm improved the training efficiency of BP-NN algorithms by adaptively modifying the initial search direction.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research