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专家系统和神经网络是计算机人工智能应用的两大分支,目前,它们的发展均表现出各自存在的问题,如专家系统知识获取的“瓶颈”问题和神经网络知识表示的“黑箱结构”问题。在教学水平智能评估系统中将神经网络和传统专家系统技术相结合,相互取长补短,这是开发教学水平智能评估系统的一条比较切实有效的途径。本文将专家系统和神经网络进行集成, 达到优势互补的目的,有效的克服了传统教学水平评估方法的不足,从而更加全面的、科学的、合理的对教学水平进行评估。
Expert systems and neural networks are the two branches of computer-based artificial intelligence. At present, their development shows their own problems, such as the “bottleneck” problem of expert system knowledge acquisition and the “black box structure” of neural network knowledge representation "problem. Combining the neural network with the traditional expert system technology in the teaching level intelligent evaluation system and mutual complementarity, this is a more effective way to develop the teaching level intelligent evaluation system. In this paper, the expert system and neural network are integrated to achieve the goal of complementarity and effectively overcome the shortcomings of the traditional teaching level assessment methods, so as to evaluate the teaching level more comprehensively, scientifically and reasonably.