Visualization of Neural Network Operation for Improving the Performance Optimization Process
This paper considers the implementation of a neural network development environment, and describes a prototype that has been produced. The prototype has been targeted at pattern classifier applications, where human support can be beneficial. The initial aim of the system is to provide support for generating a neural network solution to a particular pattern classification problem. This takes the form of supporting the supervised training of a neural network. The support is designed to speed up the training process, by providing the operator with rapid indications of network performance, for the current configuration. The development environment currently supports the Neocognitron neural network paradigm, which is a versatile pattern classifier. The operation of the layers of this network can be presented visually to the operator, as an aid to setting up. A major consideration is the use of Graphical User Interfaces, and the use of X Windows and Motif for the development environment. In conclusion, the results of the work are considered, and future developments outlined.
KeywordsGraphical User Interface Test Pattern Training Pattern Configuration File Supervise Training
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- R.P. Lippmann, “Pattern Classification Using Neural Networks”, IEEE Communications Magazine, 47–64, Nov 1989.Google Scholar
- K. Fukushima, S. Miyake, T. Ito, “Neocognitron: a neural network model for a mechanism of visual pattern recognition”, IEEE Transactions on Systems, Manand Cybernetics, SMC-13: 826–834, 1983.Google Scholar
- K. Fukushima, “A Neural Network for Visual Pattern Recognition”, Proceedings of the IEEE International Conference on Neural Networks, 65-75, 1988.Google Scholar