Fuzzy adapting vigilance parameter of ART-II neural nets

The ART-II model that self-organizes stable recognition codes in real-time is capable of recognizing arbitrary sequences. Based on the feedback mechanism in ART-II, this paper analyses its dynamical process and characteristics of convergence, and defines the concepts of attractive basin, self-stability, focus point. A fuzzy adaptive vigilance ρ algorithm, with ρ optimally tailored in signal processing under noisy environment, is proposed. The improved ART-II model with the fuzzy adaptive ρ has the capability of tolerating and correcting error in the memory while preserving the pattern sensitivity for signal recognition. The new algorithm overcomes the weakness of fixed ρ which may cause the spurious memory. An intelligent signal processing system is constructed for the recognition of multifrequency patterns in telecommunication. The result of simulation demonstrates that the ART-II model with fuzzy adaptive ρ recognizes signals at lower signal-to-noise ratio than original one with fixed ρ