知识-模糊神经网络在复杂劳动计量中的应用

由于大脑自身结构的超复杂性和人们对大脑信息处理机制与过程认识的局限性，复杂劳动计量长期以来未能得到有效解决。文中首先系统地研究了复杂劳动过程的三要素，其次通过引入知识工程原理、模糊神经网络技术等，根据大脑处理信息在微观结构与宏观形式上的基本特点及复杂劳动面向对象的信息特征，提出基于知识－模糊神经网络的复杂劳动计量思路，以计量复杂劳动量，并给出计量方法与步骤及案例分析，案例分析结果表明，该文所提出的

Research on Complex Labor Metrology Based on Knowledge and Fuzzy Neural Networks

Complex labor and simple labor are symmetric, and complex labor is a labor process in which human thinking process prevails. Because of the complexity of a man's brain in the structure and the limitation which a man recognizes the processes and principles of brain handling information, complex labor metrology has been effectively not solved for a long time. In order to solve the problem, firstly, three key factors of a complex labor process are systematically analyzed, and then knowledge engineering principles and fuzzy neural network technologies are introduced. Secondly, according to the characteristics of brain handling information in the microstructure and macro form and the informational characteristics of complex labor objects, the complex labor metrology theory and the method based on knowledge and fuzzy neural networks are presented. Finally, an example is provided. It is shown that the algorithms are convergent and the results of the example are consistent with the actual.