Brown M, Harris C J
Department of Electronics and Computer Science, University of Southampton, Hants, UK.
Int J Neural Syst. 1995 Jun;6(2):197-220. doi: 10.1142/s0129065795000159.
This paper outlines some of the theoretical and practical developments being made in neurofuzzy systems. As the name suggests, neurofuzzy networks were developed by fusing the ideas that originated in the fields of neural and fuzzy systems. A neurofuzzy network attempts to combine the transparent, linguistic, symbolic representation associated with fuzzy logic with the architecture and learning rules commonly used in neural networks. These hybrid structures have both a qualitative and a quantitative interpretation and can overcome some of the difficulties associated with solely neural algorithms which can usually be regarded as black box mappings, and with fuzzy systems where few modelling and learning theories existed. Both B-spline and Gaussian Radial Basis Function networks can be regarded as neurofuzzy systems and soft inductive learning algorithms can be used to extract unknown, qualitative information about the relationships contained in the training data. In a similar manner, qualitative rules or information about the network's structure can be used to initialise the system. These areas, coupled with the extensive work being carried out on theoretically analysing their modelling, convergence and stability properties means that this research topic is highly applicable in "intelligent" modelling and control problems. Apart from outlining this work, the paper also discusses a wide variety of open research questions and suggests areas where new efforts may be fruitfully applied.
本文概述了神经模糊系统在理论和实践方面的一些发展。顾名思义，神经模糊网络是通过融合神经和模糊系统领域的思想而发展起来的。神经模糊网络试图将与模糊逻辑相关的透明、语言、符号表示与神经网络中常用的架构和学习规则结合起来。这些混合结构具有定性和定量的解释，并且可以克服一些与单纯的神经算法（通常可视为黑箱映射）以及模糊系统（几乎没有建模和学习理论）相关的困难。B样条和高斯径向基函数网络都可以被视为神经模糊系统，软归纳学习算法可用于提取训练数据中包含的关系的未知定性信息。同样，关于网络结构的定性规则或信息可用于初始化系统。这些领域，再加上在理论上分析它们的建模、收敛和稳定性特性方面所开展的大量工作，意味着这个研究主题在“智能”建模和控制问题中具有高度的适用性。除了概述这项工作外，本文还讨论了各种各样的开放研究问题，并提出了新的努力可能会卓有成效地应用的领域。
