Abstract
Despite their diverse applications in many domains, neural networks are difficult to interpret owning the lack of mathematical models to express the 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. This type of data limits such conventional approaches when attempting to remove the 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 from the proposed algorithms with those from See5 demonstrates the effectiveness of the proposed algorithms.