Kohonen网络扩展的神经元模糊系统的隶属度函数
Obtaining Membership Functions from a Neuron Fuzzy System extended by Kohonen Network
摘要：
本文提出了一种将模糊系统技术与人工神经网络相结合的混合计算模型--Kohonen网络改进的新模糊神经元（NFN-MK）。它的主要任务是自动生成隶属函数，特别是三角形的形式，目的是一个系统的动态建模。通过模拟真实系统来测试该模型，这里用非线性数学函数表示。与传统的神经网络和模糊神经系统在系统辨识领域的相关研究结果比较表明，NFN-MK模型具有相似的性能，尽管它更简单。
英文摘要：
This article presents the Neo-Fuzzy-Neuron Modified by Kohonen Network (NFN-MK), an hybrid computational model that combines fuzzy system technique and artificial neural networks. Its main task consists in the automatic generation of membership functions, in particular, triangle forms, aiming a dynamic modeling of a system. The model is tested by simulating real systems, here represented by a nonlinear mathematical function. Comparison with the results obtained by traditional neural networks, and correlated studies of neurofuzzy systems applied in system identification area, shows that the NFN-MK model has a similar performance, despite its greater simplicity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
