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http://dx.doi.org/10.3745/KIPSTD.2009.16-D.1.139

Development of Monitoring Tool for Synaptic Weights on Artificial Neural Network  

Shin, Hyun-Kyung (경원대학교 수학정보학과)
Abstract
Neural network is a very exciting and generic framework to develop almost all ranges of machine learning technologies and its potential is far beyond its current capabilities. Among other characteristics, neural network acts as associative memory obtained from the values structurally stored in synaptic inherent structure. Due to innate complexity of neural networks system, in its practical implementation and maintenance, multifaceted problems are known to be unavoidable. In this paper, we present design and implementation details of GUI software which can be valuable tool to maintain and develop neural networks. It has capability of displaying every state of synaptic weights with network nodal relation in each learning step.
Keywords
CBSE; GUI; .NET; Artificial Neural Network;
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1 Hinton, G. E., Osindero, S., and Teh, Y., “A fast learning algorithm for deep belief nets,” Neural Computation, 18: 1527-1554.[1], 2006   DOI   ScienceOn
2 Turing, A. M., “Computing machinery and intelligence,” Mind 50: 433-460, 1950   DOI   ScienceOn
3 Hassoun, M. H., “Fundamentals of Artificial Neural Networks,” The MIT Press, Cambridge, MA, 1995
4 Bradski, G.; Kaehler, A., “Learning OpenCV: Computer Vision with the OpenCV Library,” O'Reilly, Cambridge, MA, 2008
5 Misra, M., “Parallel Environments for Implementing Neural Networks,” Neural Computing Surveys, 1:48-60, 1997
6 Caudill, M., and Butler, C., “Naturally Intelligent Systems,” The MIT Press, Cambridge, MA, 1992
7 http://www.mathworks.com/products/neuralnet/
8 http://sourceforge.net/projects/opencvlibrary/
9 Kim, S., and Lee, S., “Phase Dynamics in the Biological Neural Networks,” Physica A, 288:380-396, 2000   DOI   ScienceOn
10 Szyperski, C., “Component Software: Beyond Object-Oriented Programming. 2nd ed.,” Addison-Wesley Professional, Boston, MA, 2002
11 Heineman, G. T., and Councill, W. T., “Component-Based Software Engineering: Putting the Pieces Together,” Addison-Wesley Professional, Reading, MA, 2001
12 Stroustrup, B., “The C++ Programming Language, third Edition,” Addison-Wesley, Reading, MA, 1998
13 Bishop, C. M., “Pattern Recognition and Machine Learning,” Oxford University Press, Oxford, UK, 2007
14 Haykin, S., “Neural Network and Machine Learning,” 3rd edition, Prentice Hall, Upper Saddle River, NJ, 2008
15 Shakhnarovish, Darrell, and Indyk, “Nearest-Neighbor Methods in Learning and Vision,” The MIT Press, Cambridge, MA, 2005
16 Kohonen, T., “Self-Organizing Maps”, 2nd edition, Springer-Verlag, Berlin, 1997
17 Paulsen, O., and Sejnowski, T. J., “Natural patterns of activity and long-term synaptic plasticity.” Current opinion in neurobiology 10 (2): 172–179. 2000   DOI   ScienceOn
18 Raphael, R., “DARPA Urban Challenge, a C++ based platform for testing Path Planning Algorithms: An application of Game Theory and Neural Networks,” Cornell University, 2007
19 Sipser, M., “Introduction to the Theory of Computation,” $2^nd$ edition, Course Technology, Boston, 2005
20 Buhmann, M. D., and Ablowitz, M. J., “Radial Basis Functions: Theory and Implementations,” Cambridge University Press, London, 2003