| Summary: | 碩士 === 清雲科技大學 === 電子工程研究所 === 95 === In order to fit in humanity, the non-quantification and fuzzified methods are used to evaluate the learning performance of learner in mostly intelligent digital tutorial systems. Therefore, the fuzzy causal network model of learner used in those systems, but the following problems are existed while using the fuzzy inference in a multi-layer causal network with partial feedback: (a) There are too many membership functions need to be assigned and adjusted; (b) Above the second hidden layer, there is not physical meaning to assign and adjust those fuzzy partitions with inference independently. Dearing with those problems, a fuzzy space partition propagation method is designed, and the associated inference method also used in a multi-layer fuzzy causal network. This method has the following advantages. (a) The system will be automatically to adjust the membership function. (b) The consequent of inference in previous layer just as the antecedent part of inference in the posterior layer. This method can reduce the difficulty of artificial partition, and make the digital tutorial systems more flexible and more intelligent.
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