Mitra S, Hayashi Y
Machine Intelligence Unit, Indian Statistical Institute, Calcutta 700 035, India.
IEEE Trans Neural Netw. 2000;11(3):748-68. doi: 10.1109/72.846746.
The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.
本文是对神经模糊规则生成算法进行详尽综述的一次全新尝试。近年来，从人工神经网络生成规则越来越受到欢迎，因为它能够向用户提供一些关于网络中嵌入的符号知识的见解。模糊集有助于以更易于人类理解或自然的形式提供此类信息，并且可以处理不同层面的不确定性。神经模糊方法将连接主义方法和模糊方法的优点共生结合，在现阶段构成了软计算的一个关键组成部分。迄今为止，尚未对用于规则生成的各种神经模糊模型进行详细且综合的分类。我们提议将它们整合到一个统一的软计算框架之下。此外，我们在更宽泛的规则生成视角中纳入了规则提取和规则细化。从这个更广阔的视角出发，还考虑了为模糊推理和模糊控制学习与生成的规则。模型根据其神经模糊合成的程度进行分组。强调了遗传算法和粗糙集等其他软计算工具的使用。从最初编码一些粗略领域知识的基于模糊知识的网络生成规则，结果发现能产生更精细的规则。最后，给出了在医学诊断中的实际应用。
