Blind source separation (BSS) is a technique for recovering a set of source signals without a priori information on the transformation matrix or the probability distributions of source signals. Based on separation results of outputs, this paper proposes the interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC)-based learning rate adjustment for the BSS. The adopted T2FCMAC system has the ability of generating the proper learning rate by using the inputs of second- and higher order correlation coefficients of output components. In addition, to enhance the performance of the T2FCMAC-based learning rate approach, the T2FCMAC system is optimized by particle swarm optimization (PSO) algorithm by the performance index of second-order correlation measure. Simulation and comparison results are introduced to show the effectiveness and performance of the proposed approach.
|頁（從 - 到）||411-421|
|期刊||International Journal of Fuzzy Systems|
|出版狀態||Published - 1 9月 2014|