| Summary: | 碩士 === 朝陽科技大學 === 資訊工程系碩士班 === 92 === In this thesis, a quantum neural fuzzy network (QNFN) for classification applications is proposed. The QNFN model is a four-layer structure. Layer 2 of the QNFN model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-organizing learning algorithm, which consists of the self-clustering algorithm (SCA) and the backpropagation algorithm, is also proposed. The proposed the SCA method is a fast, one-pass algorithm for a dynamic estimation of the number of clusters in an input data space. The backpropagation algorithm is used to tune the adjustable parameters. An entropy-based quantum neural fuzzy network (EQNFN) is proposed. The EQNFN model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK) to improve performance and learning accuracy. Quantum fuzzy entropy is employed to evaluate the information on pattern distribution in the pattern space. With this information, we can determinate the number of quantum levels. A compensatory quantum neural fuzzy network (CQNFN) is proposed. The compensatory-based fuzzy reasoning method is using adaptive fuzzy operations of neural fuzzy network that can make the fuzzy logic systems more adaptive, effective and converge quickly. Finally, we are used to classification application to demonstrate our models learning capability and performance. The simulation results show that the average classification accuracy of our models is better than other methods.
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