The learning and dynamics of VSF-network

In this paper, we show an overview of VSF-network, the presumption of parameters for the additive learning, results of the learning applied to obstacle avoidance task using the presumed parameters, and we examined the state of the hidden-layer in VSF-network that the additive learning is applied. The recognition of patterns that are the learned the existing pattern, the incrementally learned pattern, and the pattern that is combined those both patterns, are improved, by setting the state of GCM-module where is a weak chaotic state in the incremental learning phase. The feature which can be recognized using the pattern that combines both the freshly learned pattern and the existing pattern that have never learned, is the key feature of VSF-network. A T-junction, a simple obstacle, and a compound obstacle were provided to a hierarchical network and VSF network that are incrementally learned, and the outputs from the hidden-layer were compared. Through the comparison, we confirmed that the output pattern of units that is incrementally learned pattern, and the combination of both patterns respectively on VSF-network.