| Summary: | 碩士 === 國立交通大學 === 工業工程與管理系 === 89 === Despite their diverse applications in many domains, neural networks are difficult to interpret owing the lack of a mathematical model to express its training result. While adopting the rule extraction method to develop different algorithms, many researchers normally simplify a network's structure and then extract rules from the simplified networks. Such conventional approaches are subject to the type of data while attempting to remove unnecessary connections. In addition to developing network pruning and extraction algorithms, this work attempts to determine the important input nodes. In the proposed algorithms, the type of input data is not limited to binary, discrete or continuous. Moreover, two numerical examples are analyzed. Comparing the results of the proposed algorithms with those of See5 demonstrates the effectiveness of the proposed algorithms.
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