Neural network based expert system model for conflict resolution

N. V Subba Reddy, P. Nagabhushan, K. C. Gowda

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

The paper describes a neural network and expert system model for conflict resolution of unconstrained handwritten characters and it completely resolves the confusion between the conflicting characters. The basic recognizer is the neural network. The neural network classifier is a combination of Modified Self-Organizing Map (MSOM) and Learning Vector Quantization (LVQ). It will solve most of the cases, but will fall in certain confusing cases. The expert system, the second recognizer, resolves the confusions generated by the neural network. The results obtained from this two-tier architecture are compared with the comments collected from an experiment conducted with a group of human experts specialized in unconstrained handwritten character recognition. The substitution error is eliminated.

Original languageEnglish
Pages229-232
Number of pages4
Publication statusPublished - 01-12-1996
EventProceedings of the 1996 Australian New Zealand Conference on Intelligent Information Systems - Adelaide, Aust
Duration: 18-11-199620-11-1996

Conference

ConferenceProceedings of the 1996 Australian New Zealand Conference on Intelligent Information Systems
CityAdelaide, Aust
Period18-11-9620-11-96

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All Science Journal Classification (ASJC) codes

  • Software

Cite this

Reddy, N. V. S., Nagabhushan, P., & Gowda, K. C. (1996). Neural network based expert system model for conflict resolution. 229-232. Paper presented at Proceedings of the 1996 Australian New Zealand Conference on Intelligent Information Systems, Adelaide, Aust, .