神经网络和理性卢卡谢维奇逻辑

2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622) Pub Date : 2002-08-07 DOI:10.1109/NAFIPS.2002.1018111

P. Amato, A. di Nola, B. Gerla

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引用次数: 14

摘要

我们描述了有理Lukasiewicz公式与神经网络之间的对应关系，其中激活函数是截断恒等函数，突触权重是有理数。一方面，拥有神经网络的逻辑表示(在给定的逻辑中)可以扩大这些对象的可解释性、可合并性和重用性。另一方面，神经网络可以用来从数据中学习公式，并作为公式(用公式表示的函数)的回路对应。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Neural networks and rational Lukasiewicz logic

We describe a correspondence between rational Lukasiewicz formulas and neural networks in which the activation function is the truncated identity and synaptic weights are rational numbers. On one hand, having a logical representation (in a given logic) of neural networks could widen the interpretability, amalgamability and reuse of these objects. On the other hand, neural networks could be used to learn formulas from data and as circuital counterparts of (functions represented by) formulas.

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2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)

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