The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional-type SIRMs method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs methods can not be applied to XOR (Exclusive OR). In this paper, we propose a "kernel-type SIRMs method" which uses the kernel trick to the SIRMs method, and show that this method can treat XOR. Further, a learning algorithm of the proposed SIRMs method is derived by using the steepest descent method, and compared with the one of conventional SIRMs method and kernel perceptron by applying to identification of nonlinear functions, medical diagnostic system and discriminant analysis of Iris data.

In this paper, we propose an adaptive multistage fuzzy-based partial parallel interference cancellation (FB-PPIC) multiuser detector for multi-carrier direct-sequence code-division multiple-access (MC-CDMA) communication systems over frequency selective fading channels. The partial cancellation tries to reduce the cancellation error in parallel interference cancellation (PIC) schemes due to the wrong interference estimations in the early stages and thus outperforms the conventional PIC (CPIC) under the heavy load for MC-CDMA systems. Therefore, in this paper, the adaptive cancellation weights are inferred from a proposed multistage fuzzy inference system (FIS) to perform effective PPIC multiuser detection under time-varying frequency selective fading channels in MC-CDMA systems. Simulation results show that the proposed adaptive four-stage FB-PPIC scheme outperforms both CPIC and constant weight PPIC (CW-PPIC) schemes, especially in near-far environments.