Yuko Osana

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In this paper, we propose a Chaotic Complex-valued Bidirectional Associative Memory (CCBAM) which can realize one-to-many associations of multi-valued patterns. The proposed model is based on the Bidirectional Associative Memory, and is composed of complex-valued neurons and chaotic complex-valued neurons. In the proposed model, associations of multi-valued(More)
—In this paper, we propose a Kohonen feature map associative memory with area representation for sequential patterns. This model is based on the Kohonen feature map associative memory with area representation and the Kohonen feature map associative memory for temporal sequences. The proposed model can learn sequential patterns successively, and has(More)
—In this paper, we propose a chaotic complex-valued associative memory which can realize a dynamic association of multi-valued patterns. The proposed model is based on a complex-valued associative memory and a chaotic associative memory. The complex-valued asso-ciative memory can treat multi-valued patterns, and the chaotic associative memory can recall(More)
In this paper, we propose a hetero chaotic associative memory for successive learning with multi-winners competition (HCAMSL-MW). The proposed model is based on a hetero chaotic associative memory for successive learning (HCAMSL) and the multi winners self-organizing neural network (MWSONN). In most of the conventional neural network models, the learning(More)
In this paper, we propose a successive learning method in hetero-associative memories, such as Bidirectional Associative Memories and Multidirectional Associative Memories, using chaotic neural networks. It can distinguish unknown data from the stored known data and can learn the unknown data successively. The proposed model makes use of the difference in(More)
In biological systems formed by living cells, the small populations of some reactant species can result in inherent randomness which cannot be captured by traditional deterministic approaches. In that case, a more accurate simulation can be obtained by using the Stochastic Simulation Algorithm (SSA). Many stochastic realizations are required to capture(More)
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(More)
In this research, we propose a self-organizing map with refractoriness (SOMR) and apply it to a similarity-based image retrieval. The proposed SOMR is based on the self-organizing map (SOM) and the refractoriness which is observed in the real neuron is introduced. In the proposed SOMR, the plural neurons in the Map Layer corresponding to the input can fire(More)