Multilevel feature-based fuzzy fusion for target recognition

Information fusion includes the integration of feature data, expert knowledge, and algorithms. For example, in automatic target recognition features of size, color, and motion can be fused to assess the combination of multi-modal information. A neurofuzzy fusion of features captures the multilevel language content of sensory information by fusing neural network data analysis with rule-based decision making. Additionally, the neurofuzzy architecture can effectively fuse coarse and fine abstracted feature data at the content level for decision making. In this paper, we investigate a multilevel neuro-fuzzy feature-based architecture for synthetic aperture radar target recognition.

[1]  R. Setiono.  Extracting Rules from Pruned Neural Networks for Breast Cancer Diagnosis , 1996 .

[2]  Michael C. Mozer,et al.  Using Relevance to Reduce Network Size Automatically , 1989 .

[3]  Gavriel Salvendy,et al.  Cognitive engineering based knowledge representation in neural networks , 1991 .

[4]  Avelino J. Gonzalez,et al.  The engineering of knowledge-based systems: theory and practice , 1993 .

[5]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[6]  James Llinas,et al.  Multisensor Data Fusion , 1990 .

[7]  Mica R. Endsley,et al.  Providing understanding of the behavior of feedforward neural networks , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[8]  Erik Blasch,et al.  Sensor fusion cognition using belief filtering for tracking and identification , 1999, Defense, Security, and Sensing.

[9]  David E. Rumelhart,et al.  Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..

[10]  Nikola K. Kasabov,et al.  Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems , 1996, Fuzzy Sets Syst..

[11]  R. Nakano,et al.  Medical diagnostic expert system based on PDP model , 1988, IEEE 1988 International Conference on Neural Networks.

[12]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[13]  Adedeji Badiru,et al.  Expert Systems Applications in Engineering and Manufacturing , 1998 .

[14]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[15]  F. Maire,et al.  A partial order for the M-of-N rule-extraction algorithm , 1997, IEEE Trans. Neural Networks.

[16]  Ignacio Requena,et al.  Are artificial neural networks black boxes? , 1997, IEEE Trans. Neural Networks.

[17]  S. H. Huang,et al.  Artificial neural networks in manufacturing: concepts, applications, and perspectives , 1994 .

[18]  LiMin Fu,et al.  Rule Learning by Searching on Adapted Nets , 1991, AAAI.

[19]  Stephen I. Gallant,et al.  Neural network learning and expert systems , 1993 .

[20]  Anne Treisman,et al.  Features and objects in visual processing , 1986 .

[21]  Anil Mital,et al.  Handbook of Expert Systems Applications in Manufacturing Structures and Rules , 1994 .

[22]  A Treisman,et al.  Feature analysis in early vision: evidence from search asymmetries. , 1988, Psychological review.

[23]  Susan L. Franzel,et al.  Guided search: an alternative to the feature integration model for visual search. , 1989, Journal of experimental psychology. Human perception and performance.