Halgamuge S K, Glesner M
Darmstadt University of Technology, Department of Computer Engineering, Germany.
Int J Neural Syst. 1995 Jun;6(2):185-96. doi: 10.1142/s0129065795000147.
Research in fuzzy neural networks, which started from application oriented fuzzy system tuning, then moving to the automatic generation of fuzzy systems from data, is reaching a more mature stage, especially after the proof of functional equivalence of certain fuzzy models and neural networks. It is essential that the applicability of such developments is explored emphasizing the directions that research should follow. It can be shown that the nearest prototype classifier is functionally equivalent to an alternative fuzzy classifier model. Efficient, hardware friendly training algorithms are developed for dynamic generation of an optimum number of nearest prototypes for neural classifiers which enable the generation of fuzzy systems in real time. These systems are tested with complex applications showing the simulation results.
模糊神经网络的研究始于面向应用的模糊系统调整，随后发展到从数据自动生成模糊系统，目前正进入一个更加成熟的阶段，特别是在证明了某些模糊模型和神经网络的功能等效性之后。探索此类发展的适用性并强调研究应遵循的方向至关重要。可以证明，最近邻原型分类器在功能上等同于另一种模糊分类器模型。针对神经分类器动态生成最佳数量的最近邻原型，开发了高效、硬件友好的训练算法，从而能够实时生成模糊系统。这些系统通过复杂应用进行了测试，并展示了仿真结果。
