Implementation of Dynamics Neural Network in Solving Inverse Function

C  K  Ang; M  I  Solihin and T  H  Tan

doi:10.17485/ijst/2016/v9iS1/110176

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Implementation of Dynamics Neural Network in Solving Inverse Function

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2016/v9iS1/110176

Year: 2016, Volume: 9, Issue: Special Issue 1, Pages: 1-6

Original Article

Implementation of Dynamics Neural Network in Solving Inverse Function

C. K. Ang^* , M. I. Solihin and T. H. Tan

Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia; [email protected]
[email protected]
[email protected]
*Author for correspondence
Ang
Faculty of Engineering
Email: [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Back-Propagation Neural Network (BPNN) has been widely used in solving nonlinear problems. However, there are some limitations in using conventional BPNN especially for high order nonlinear problems. Dynamic Back-Propagation Neural Network (DBPNN) is proposed in this paper to improve the performance of conventional BPNN. Its adaptive learning ability is closer to human being learning behavior in comparing to conventional BPNN. Few simulations have been run to test the robustness of DBPNN and the results are compared to the conventional BPNN
Keywords: Artificial Intelligences, Neural Networks, Nonlinear Function