| Summary: | 碩士 === 元智大學 === 電機工程學系 === 101 === This thesis proposes the interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC)-based learning rate adjustment for the blind source separation (BSS). To enhance the performance of the T2FCMAC-based learning rate, the T2FCMAC system is optimized by PSO algorithm. Recently, independent component analysis (ICA) algorithms have been proposed to solve the BSS problems. The gradient algorithm is a popular method deals with separating independent signal step by step with learning rate. In order to balance the mis-adjustment and the speed of convergence, the learning rate will be computed by T2FCMAC with input of the second-order and higher order correlation coefficients of output components. The T2FCMAC system is a more generalized network with better learning ability to provide the adaptive learning rate of the BSS. Furthermore, the method we proposed can be implemented in the image encryption. According to the underdetermined BSS problem, the original images are encrypted by key images to ensure the security of the cryptosystem. Finally, we present the T2FCMAC-based learning rate for the BSS and implement it in the image encryption system.
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