Abstract
The well-known ANFIS (adaptive-network-based fuzzy inference system) has demonstrated good performance and applicability. This chapter presents a quantum version of ANFIS model, namely, qANFIS, whose network structure is similar to ANFIS but it is with quantum membership functions (qMF). A hybrid learning procedure is proposed to update the parameters. First, we develop a genetic algorithm with trimming operator to determine the number of quantum levels in qMF. Second, the least squares estimate method is applied to update the consequent parameters. Finally, we introduce two methods to update the qMF parameters, the gradient descent and the quantum-inspired particle swarm optimization methods, and one of them is selected for training purpose. The simulation results of path planning had demonstrated that the proposed method could reach satisfactory performance and smaller error measure as compared with ANFIS.
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Acknowledgments
This work was supported by the National Science Council of Taiwan (R.O.C.) under grant no. NSC 100-2410-H-364-002.
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Chiang, CH. (2013). Quantum-Membership-Function-Based Adaptive Neural Fuzzy Inference System. In: Juang, J., Huang, YC. (eds) Intelligent Technologies and Engineering Systems. Lecture Notes in Electrical Engineering, vol 234. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6747-2_28
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DOI: https://doi.org/10.1007/978-1-4614-6747-2_28
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