Fusion ARTMAP: an adaptive fuzzy network for multi-channel classification - Industrial Fuzzy Control and Intelligent Systems, 1993., IFIS '93., Third International Conference

Fusion ARTMAP is a selforganizing 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. I. MULTI-CHANNEL DATA FUSION Fusion ARTMAP is a neural network architec‘Supported in part by ARPA (ONR NO0014-92-J-4015), the National Science Foundation (NSF IRI 90-00530), and the Office of Naval Research (ONR N00014-91-J-4100). tsupportedin part by British Petroleum (BP 89-A-1204), ARPA (ONR N00014-92-5-4015), the National Science Foundation (NSF IRI-90-00530),and the Office of Naval Re*earch isupported in part by ARPA (ONR N00014-92-J-101J), the National Science Foundation (NSF IRI-90-2487?’), and the Office of Naval Research (ONR N00014-91-J-4100). §Supported in part by the Air Force Office of Scientific Research (AFOSR F49620-92J-0334), a National Science Foundation Graduate Fellowship, and the Office of Naval Research (ONR N0014-913-4 100). (ONR N00014-91-J-4100). 0-7803-1485-9/93$03.0