Chaotic complex-valued multidirectional associative memory with adaptive scaling factor

In this paper, we propose a Chaotic Complex-valued Multidirectional Associative Memory (CCMAM) with adaptive scaling factor. The proposed model is based on the conventional CCMAM with variable scaling factor. In the conventional CCMAM with variable scaling factor, the scaling factor of refractoriness is determined based on the time. In contrast, in the proposed model, the scaling factor of refractoriness is determined based on not only the time but also the internal states of neurons. The proposed model is composed of complex-valued neurons and chaotic complex-valued neurons, and can realize one-to-many associations of M-tuple multi-valued patterns as similar as the conventional CCMAM with variable scaling factor. We carried out a series of computer experiments and confirmed that the proposed model can determine the scaling factor of refractoriness automatically and its one-to-many association ability almost equals to that of the well-turned CCMAM with variable scaling factor.