review: competitive learning algorithm of neural network

IV. CONCLUSION AND FUTURE WORK

Finally, simple, single layer networks of multiple interconnected units are considered in the context of competitive learning, learning vector quantization, principal component analysis, and self-organizing feature maps. Simulations are also included which are designed to illustrate the powerful emerging computational properties of these simple networks and their application. It is demonstrated that local interactions in a competitive net can lead to global order. A case in point is the SOFM where simple incremental interactions among locally neighboring units lead to a global map which preserves the topology and density of the input data. This paper considers the use of learning vector quantization to model aspects of development including of the property of recurrent rules of learning for strengthening of synaptic efficacy of structure in the visual system in early life. Refference [1] Lo,J.T.-H.; “Unsupervised Hebbian learning by recurrent multilayer neural networks for temporal hierarchical pattern recognition” Information Sciences and Systems (CISS), 2010 44th Annual Conference on Digital Object Identifier: 10.1109/CISS.2010.5464925 Publication Year: 2010 , Page(s): 1 – 6. [2] Tung-Shou Chen; Jeanne Chen; Yuan-Hung Kao; Bai-JiunTu; “A Novel Anti-Competitive Learning Neural Network Technique against Mining Knowledge from Databases”Software Engineering, 2009. WCSE '09. WRI World Congress on Volume:4DigitalObjectIdentifier: 10.1109 / WCSE . 2009.345 Publication Year: 2009 , Page(s): 383 – 386. [3]Shou-weiLi; “Analysis of Contrasting Neural Network with Small-World Network” Future Information Technology and Management Engineering, 2008. FITME '08. International Seminar on Digital Object Identifier: 10.1109/FITME.2008.55 Publication Year: 2008 , Page(s): 57 - 60 [4] Esakkirajan, S.; Veerakumar, T.; Navaneethan, P.; “Adaptive vector quantization technique for retinal image compression” Computing , Communication and Networking, 2008. ICCCn 2008. International Conference on Digital ObjectIdentifier: 10.1109 / ICCCNET.2008.4787724 Publication Year: 2008 , Page(s): 1 - 4 . [5]Chaudhuri, A.; De, K.; Chatterjee, D.; “A Study of the Traveling Salesman Problem Using Fuzzy Self

Organizing Map”Industrial and Information Systems, 2008. ICIIS 2008. IEEE Region 10 and the Third international Conference on Digital Object Identifier: 10.1109 / ICIINFS .2008. Publication Year: 2008 , Page(s): 1 - 5. [6]Kamimura,R.; “Controlled Competitive Learning: Extending Competitive Learning to Supervised Learning”Neural Networks, 2007. IJCNN 2007. International Joint Conference on Digital Object Identifier: 10.1109 / IJCNN. 2007 . 4371225 Publication Year: 2007 , Page(s): 1767 – 1773. [7]Daxin Tian; Yanheng Liu; Da Wei; Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on Volume:1 Digital Object Identifier: 10.1109 / WCICA .2006.1712893 Publication Year: 2006 , Page(s): 2886 - 2890. [8]Sutton, G.G., III; Reggia, J.A.; Maisog, J.M.; “Competitive learning using competitive activation rules” Neural Networks, 1990., 1990 IJCNN International Joint Conference on Digital Object Identifier: 10.1109/IJCNN.1990.137728 Publication Year: 1990 , Page(s): 285 – 291.

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ISSN:2229-6093