International Journal of Mathematics Trends and Technology
Volume 27 | Number 1 | Year 2015 | Article Id. IJMTT-V27P503 | DOI : https://doi.org/10.14445/22315373/IJMTT-V27P503
Modern digital computers outperform humans in tasks based on precise and fast arithmetic operations. However, people are much better and faster than computers in solving complex perceptual problems, such as recognizing images, often in the presence of disturbances. Also, humans can perform complex movements with precision and grace, even in the presence of disturbances, and can generalize from past experience. In a computer, usually there exists a single processor implementing a sequence of arithmetic and logical operations, nowadays at speeds approaching billion operations per second. However this type of devices has ability neither to adapt their structure nor to learn in the way that human being does. An Artificial Neural Model (ANM) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel mathematical structure of the information processing system. In this paper we focus on the mathematical modeling aspects of the basic unit of the human nervous system and Artificial Neural Model.
[1] Muhammad Hanif, „‟ Unconstrained Nonlinear Optimization Methods for Feedforward Artficial Neural Networks”, Post Doctoral Research Report , University Grants Commission Bangladesh (2015), pp. 120-179
[2] Muhammad Hanif and Syeda Latifa Parveen, “Newton‟s Method for Feedforward Artificial Neural Networks”, USTA (2014), V- 21, Number 1, pp. 59-67.
[3] Muhammad Hanif, Md Jashim Uddin, Md Abdul Alim „‟ The Method of Steepest Descent for Feedforward Artificial Neural Networks”, IOSR Journals(2014), V- 10, Issue 1, pp. 53-61
[4] Muhammad Hanif and Jamal Nazrul Islam „‟ First-Order Optimization Method for Single and Multiple-Layer Feedforward Artificial Neural Networks”, Journal of the Bangladesh Electronics Society (2010), V- 10, Number 1-2, pp. 33-47.
[5] Carpenter, G. and Grossberg, S. (1998), The Handbook of Brain Theory and Neural Networks, (ed. M.A. Arbib), MIT Press, Cambridge, MA, pp.79–82.
[6] X. H. Hu and G. A. Chen (1997), “Efficient backpropagation learning using optimal learning rate and momentum", Neural Networks 10(3), 517-527.
[7] P. Mehra and B. W. Wah, “Artificial neural networks: concepts and theory,” IEEE Comput. Society Press, (1992).
[11] R. Battiti (1992), “First and second-order methods for learning: Between steepest descent and Newton‟s methods," Neural Computation 4, 141-166.
[12] J.S. Judd, “Neural Network Design and the complexity of Learning,” MIT Press, Cambridge, MA (1990).
[13] P.R. Adby and M.A.H. Dempster, “Introduction to Optimization Methods,” Haisted Press, New York (1974).
[14] McCulloch, W. and W. Pitts (1943), A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics, 5, 115-133.
[15] Ankit Anand, Shruti, Kunwar Ambarish Singh (2014), An Efficient Approach for Steiner Tree Problem by Genetic Algorithm, SSRG International Journal of Computer Science and Engineering , volume2 issue5 , 233-240.
[16] Jinesh K. Kamdar(2014), Human Computer Interaction - A Review , SSRG International Journal of Computer Science and Engineering , volume1 issue7, 27-30
Muhammad Hanif, "Mathematics for Human Nervous System," International Journal of Mathematics Trends and Technology (IJMTT), vol. 27, no. 1, pp. 12-18, 2015. Crossref, https://doi.org/10.14445/22315373/IJMTT-V27P503
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