Abstract
In this paper, a TSK-type quantum neural fuzzy network (TQNFN) for temperature control is proposed. The TQNFN model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the TQNFN 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-constructing 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. Simulation results have been given to illustrate the performance and effectiveness of the proposed model.
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