Ben Romdhane L. A softmin-based neural model for causal reasoning.
IEEE TRANSACTIONS ON NEURAL NETWORKS 2006;
17:732-44. [PMID:
16722176 DOI:
10.1109/tnn.2006.872350]
[Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper extends a neural model for causal reasoning to mechanize the monotonic class. Hence, the resulting model is able to solve multiple, varied causal problems in the open, independent, incompatibility and monotonic classes. First, additivity between causes is formalized as a fuzzy AND-ing process. Second, an activation mechanism called the "softmin" is developed to solve additive interactions. Third, the softmin is implemented within a neural architecture. Experimental results on real-world and artificial problems reveal a good performance of the model and should stimulate future research.
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