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Issue No.05 - October (1995 vol.7)

pp: 683-690

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/69.469828

ABSTRACT

<p><it>Abstract</it>—A systematic approach has been developed to construct neural networks for qualitative analysis and reasoning. These neural networks are used as specialized parallel distributed processors for solving constraint satisfaction problems. A typical application of such a neural network is to determine a reasonable change of a system after one or more of its variables are changed. A six-node neural network is developed to represent fundamental qualitative relations. A larger neural network can be constructed hierarchically for a system to be modeled by using six-node neural networks as building blocks. The complexity of the neural network building process is thus kept manageable. An example of developing a neural network reasoning model for a transistor equivalent circuit is demonstrated. The use of this neural network model in the equivalent circuit parameter extraction process is also described.</p>

INDEX TERMS

Knowledge representation, qualitative reasoning, constraint satisfaction, parallel distributed processing, neural networks, expert systems.

CITATION

Mankuan Vai, Zhimin Xu, "Representing Knowledge by Neural Networks for Qualitative Analysis and Reasoning",

*IEEE Transactions on Knowledge & Data Engineering*, vol.7, no. 5, pp. 683-690, October 1995, doi:10.1109/69.469828