Gori Marco, Petrosino Alfredo
Dipartimento di Ingegneria dell'Informazione, Università di Siena, 53100 Siena, Italy.
IEEE Trans Neural Netw. 2004 Nov;15(6):1435-49. doi: 10.1109/TNN.2004.837585.
Fuzzy neural systems have been a subject of great interest in the last few years, due to their abilities to facilitate the exchange of information between symbolic and subsymbolic domains. However, the models in the literature are not able to deal with structured organization of information, that is typically required by symbolic processing. In many application domains, the patterns are not only structured, but a fuzziness degree is attached to each subsymbolic pattern primitive. The purpose of this paper is to show how recursive neural networks, properly conceived for dealing with structured information, can represent nondeterministic fuzzy frontier-to-root tree automata. Whereas available prior knowledge expressed in terms of fuzzy state transition rules are injected into a recursive network, unknown rules are supposed to be filled in by data-driven learning. We also prove the stability of the encoding algorithm, extending previous results on the injection of fuzzy finite-state dynamics in high-order recurrent networks.
在过去几年中，模糊神经系统一直是人们非常感兴趣的主题，因为它们能够促进符号域和亚符号域之间的信息交换。然而，文献中的模型无法处理符号处理通常所需的信息结构化组织。在许多应用领域中，模式不仅是结构化的，而且每个亚符号模式基元都附有一个模糊度。本文的目的是展示如何为处理结构化信息而适当构思的递归神经网络能够表示非确定性模糊前沿到根树自动机。当以模糊状态转换规则表示的现有先验知识被注入到递归网络中时，未知规则应该通过数据驱动学习来填补。我们还证明了编码算法的稳定性，扩展了先前关于在高阶递归网络中注入模糊有限状态动力学的结果。
