Role of Recognizing Patterns with Neural Network Approach

Sunny Thukral*
Assistant Professor, PG Department of Computer Science and Information Technology, DAV College, Amritsar, India.
Periodicity:June - August'2017
DOI : https://doi.org/10.26634/jit.6.3.13774

Abstract

This paper is based upon the recognition of either characters or digits by using Neocognitron algorithm, which can be used for detecting multiple patterns. A large pattern can be divided into multiple parts until it becomes a single cell for each and every block for its representation. Neural Network can be implemented with the concept of fuzzy logic which deals with ambiguous problems. The integration of Neural Network and Fuzzy Logic will generate such type of intelligent systems for mankind in optimum real life problems. It also describes various types of architects that can be constructed with neural network. In Neocognitron approach, a type of hierarchal network where two types of cells can be used like simple and complex cells, which can be trained by using different neural network algorithm approaches. It also describes various types of applications that can be used in real life through Neocognitron. The objective of the paper is to describe various architectures of Neural Network approach, so that it can be implemented with different algorithms for better outcomes in real life complex problems.

Keywords

Neocognitron, Artificial Neural Network, Fuzzy Logic, Pattern Recognition.

How to Cite this Article?

Thukral. S. (2017). Role of Recognizing Patterns with Neural Network Approach. i-manager’s Journal on Information Technology, 6(3), 1-7. https://doi.org/10.26634/jit.6.3.13774

References

[1]. Ali, H. K., & Mohammed, E. Z. (2010). Design artificial neural network using FPGA. IJCSNS, 10(8), 88-92.
[2]. Azeem, M. M., & Mohammad, A. (2015). An Analysis of Applications and Possibilities of Neural Networks (Fuzzy, Logic and Genetic Algorithm) in Finance and Accounting. Donnish Journal of Business and Finance Management Research, 1(2), 9-18.
[3]. Fukushima, K. (2001). Recognition of partly occluded patterns: A neural network model. Biological Cybernetics, 84(4), 251-259. Retrieved from http://simulation.visiome. neuroinf.jp/modules/visiomeSS/?s=fukushima
[4]. Fukushima, K., & Miyake, S. (1982). Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In Competition and Cooperation in Neural Nets (pp. 267-285). Springer, Berlin, Heidelberg. Retrieved from http://www.cs.princeton.edu/ courses/archive/spr08/cos598B/Readings/Fukushima198 0.pdf
[5]. Gurney, K. (1997a). An Introduction to Neural Networks. CRC Press.
[6]. Gurney, K. & Press, U. (1997b). An Introduction to Neural Networks.
[7]. Kriesel, D. (2007). A brief Introduction on Neural Networks. http://www.dkriesel.com/_media/science/ neuronalenetze-en-zeta2-2coldkrieselcom.pdf
[8]. Kwan, H. K., & Cai, Y. (1994). A fuzzy neural network and its application to pattern recognition. IEEE Transactions on Fuzzy Systems, 2(3), 185-193.
[9]. Maren, A. J., Harston, C. T., & Pap, R. M. (2014). Handbook of Neural Computing Applications. Academic Press. Retrieved from https://www.google.co.in/ search?tbo=p&tbm=bks&q=isbn:148326484X
[10]. Neocognitron Description. Retrieved from http://ftparmy.com/75732-neocognitron.html
[11]. Neocognitron. Retrieved from http://www.scholar pedia.org/article/Neocognitron#
[12]. Rowley, H. A., Baluja, S., & Kanade, T. (1998). Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 23-38.
[13]. Sadhukhan, S., Dhadekar, M., & Bhonar, S. (2016). Stock Market Prediction using Artificial Neural Networks. Imperial Journal of Interdisciplinary Research, 2(5), 645- 647. Retrieved from http://www.onlinejournal.in/ IJIRV2I5/116.pdf
[14]. Sadykhov, R. K., & Vatkin, M. E. (2001). An application of "neocognitron" neural network for integral chip image processing. In Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, International Workshop on, 2001. (pp. 207- 210). IEEE.
[15]. The logic of thinking. Part 3: Perceptron, convolutional network. Retrieved from https://bashny.net/ t/en/155988?page=3
[16]. Thukral, S. (n.d.). Neocognitron: Pattern Recognition In Neural Networks. Retrieved from http://ijsst.lkc.ac.in/p8 thukral.pdf
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.