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Author(s): Asfour, Y.R. | Carpenter, G.A. | Grossberg, S. | Lesher, G.W. |
Year: 1993
Citation: Proceedings of the Third International Conference on Industrial Fuzzy Control and Intelligent Systems (IFIS 93, Houston), Piscataway, NJ: IEEE Press, 155-160.
Abstract: Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called parallel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally-controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network
Topics:
Machine Learning,
Applications:
Information Fusion,
Models:
ARTMAP,
Modified ART,
