Improved Kohonen Feature Map Probabilistic Associative Memory based on Weights Distribution

In this paper, we propose an Improved Kohonen Feature Map Probabilistic Associative Memory based on Weights Distribution (IKFMPAM-WD). This model is based on the conventional Kohonen Feature Map Probabilistic Associative Memory based on Weights Distribution (KFMPAM-WD). The proposed model can realize probabilistic association for the training set including one-to-many relations. Moreover, this model has enough robustness for noisy input and damaged neurons. We carried out a series of computer experiments and confirmed the effectiveness of the proposed model.