根据电弧炉的熔炼工艺特性对电弧炉各种炉况进行研究，并给出了一种基于专家系统的电弧炉炉况判断方法。
Based on the research of smelting technics and furnace state, this thesis gives an EAF state judgement method based on expert system.
专家系统能够运用专家的知识与经验进行推理、判断和决策，是解决生物量软测量混合模型构建的有效方法。
Expert system can use experts' knowledge and experience to ratiocinate, judge and make decision, which is an effective way to construct the soft-sensor hybrid model.
本文的主要贡献有：一是设计了智能专家咨询系统，依据知识库，通过推理、判断，实现对农户产前、产中、产后的生产指导；
The main contributions of this article are:First, it designs the Intelligent Expert Consultation System. With the repository, it can instruct the farmers in their field work by reasoning and judgment.
因此探索建立神经网络高炉异常炉况判断专家系统是当前的研究方向。
It is the current research direction to set up the neural network expert system of judging the abnormal state of blast furnace.
根据各种故障的特征设计出专家系统用来故障信息的提取和判断。
Expert system designed according to features of faults is used to extract and analyze the faults.
数据资料库主要为专家系统提供所需要的数据资料，为专家系统作逻辑判断提供依据。
The action of the database provides data and basis of logic judge for the Expert system.
将神经网络与传统专家系统有机地结合，建立了用于高炉炉况预测与判断的神经网络专家系统。
An expert system based on neural networks for predicting and judging the state of blast furnace is developed by applying the method of combining neural networks with traditional expert system.
为了解决这个问题，研究和建立人工智能的卡钻事故专家系统是非常有必要的，它可以帮助现场工作人员准确地判明卡钻的类型，辅助经验尚不丰富的人员进行判断。
To solve this problem , it is necessary to study and establish expert system of pipe-sticking accident based on artificial intelligence, and it can tell workers the type of pipe-sticking.
采用了面向对象的知识表示方法，建立了黄土高原地区种植制度设计专家系统，系统以热量（生育期）和降水作为具体地区种植制度可行与否的判断参数。
The FSRS is designed by the object-oriented method in which the condition that determines a farming system feasible or unfeasible are accumulate temperature (growth period) and precipitation.
本课题旨在建立神经网络高炉异常炉况判断专家系统，并解决传统专家系统在知识获取方面的“瓶颈”问题，使其具有在线学习能力。
The object of this research is to set up the neural network expert system of judging the abnormal state in acquiring knowledge , so to make it have the ability of self-learning on-line.
本课题旨在建立神经网络高炉异常炉况判断专家系统，并解决传统专家系统在知识获取方面的“瓶颈”问题，使其具有在线学习能力。
The object of this research is to set up the neural network expert system of judging the abnormal state in acquiring knowledge , so to make it have the ability of self-learning on-line.
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