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Proceedings Paper

Maximum-likelihood adaptive neural network (MLANS) application to sensor fusion
Author(s): Leonid I. Perlovsky
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Paper Abstract

In this paper we discuss an approach to solving sensor and information fusion problems using the maximum likelihood adaptive neural system (MLANS). This neural network combines a model-based approach with optimal statistical techniques to achieve adaptivity in the fusion of the data. In this neural network, the weights are fuzzy measures associating each piece of information with various decision classes. This permits the fusion of data from various sources, and on various levels. These levels include measurements, features, decisions such as subjective probabilities obtained from external sources, or other fuzzy measures of association. The weights are parameterized in terms of a relatively small number of model parameters. These parameters are estimated by the minimum entropy neuron and maximum likelihood neurons. The maximum likelihood neurons permit extremely fast learning of data distributions so that MLANS achieves the information-theoretic bounds on speed of adaptation and learning. Another related advantage of a model-based approach is the MLANS capability to combine self-learning with any available information, including a priori and a posteriori information.

Paper Details

Date Published: 10 June 1994
PDF: 4 pages
Proc. SPIE 2232, Signal Processing, Sensor Fusion, and Target Recognition III, (10 June 1994); doi: 10.1117/12.177732
Show Author Affiliations
Leonid I. Perlovsky, Nichols Research Corp. (United States)


Published in SPIE Proceedings Vol. 2232:
Signal Processing, Sensor Fusion, and Target Recognition III
Ivan Kadar; Vibeke Libby, Editor(s)

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