Ben Romdhane Lotfi
Department of Computer Science, University of Sherbrooke, QC, Canada.
IEEE Trans Neural Netw. 2006 May;17(3):732-44. doi: 10.1109/TNN.2006.872350.
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.
本文扩展了一种用于因果推理的神经模型，以使单调类机械化。因此，所得模型能够解决开放、独立、不相容和单调类中的多个不同因果问题。首先，将原因之间的可加性形式化为一个模糊与运算过程。其次，开发了一种称为“软最小”的激活机制来解决加性相互作用。第三，在神经架构中实现软最小。关于现实世界和人工问题的实验结果表明该模型具有良好的性能，应该会激发未来的研究。
