Due to the competition and high cost associated with die casting defects, it is urgent to adopt a rapid and effective method for defect analysis. In this research, a novel expert network approach was proposed to avoid...Due to the competition and high cost associated with die casting defects, it is urgent to adopt a rapid and effective method for defect analysis. In this research, a novel expert network approach was proposed to avoid some disadvantages of rule-based expert system. The main objective of the system is to assist die casting engineer in identifying defect, determining the probable causes of defect and proposing remedies to eliminate the defect. 14 common die casting defects could be identified quickly by expert system on the basis of their characteristics. BP neural network in combination with expert system was applied to map the complex relationship between causes and defects, and further explained the cause determination process. Cause determination gives due consideration to practical process conditions. Finally, corrective measures were recommended to eliminate the defect and implemented in the sequence of difficulty.展开更多
The coagulation process is one of the most important stages in water treatment plant, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw wate...The coagulation process is one of the most important stages in water treatment plant, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, PH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Based on neural network and rule models, an expert system for determining the optimum chemical dosage rate is developed and used in a water treatment work, and the results of actual runs show that in the condition of satisfying the demand of drinking water quality, the usage of coagulant is lowered.展开更多
In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty ...In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.展开更多
In this paper, we propose a formal definition, general structure and work principle of the Neural Network Expert System (NNES) based on joint-type knowledge representation, and show a practical application example usi...In this paper, we propose a formal definition, general structure and work principle of the Neural Network Expert System (NNES) based on joint-type knowledge representation, and show a practical application example using NNES for forecasting the water invasion of coal mine.展开更多
The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrati...The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrations was proposed, which uses neural networks, rule models and a single loop control scheme. First, the process was described and the strategy that features an expert controller and three single loop controllers was explained. Next, neural networks and rule models were constructed based on statistical data and empirical knowledge on the process. Then, the expert controller for determining the optimal concentrations was designed through a combination of the neural networks and rule models. The three single loop controllers used the PI algorithm to track the optimal concentrations. Finally, the implementation of the proposed strategy were presented. The run results show that the strategy provides not only high purity metallic zinc, but also significant economic benefits.展开更多
Using expert systems in intelligent CAD of electrical machines have limitations such as knowledge acquisition bottlenecks and matching conflict, combinatorial explosion, and endless recursion in the reasoning process....Using expert systems in intelligent CAD of electrical machines have limitations such as knowledge acquisition bottlenecks and matching conflict, combinatorial explosion, and endless recursion in the reasoning process. This paper discusses the principle of a hybrid system of a neural network and an expert system (HNNES), i.e., knowledge representation, reasoning mechanism, and knowledge acquisition based on neural networks. An architecture of HNNES is presented in consideration of the feature of the design of electrical machines.展开更多
In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule sampl...In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.展开更多
By combining the artificial neural network with the rule reasoning expert system, an expert diagnosing system for a rotation mechanism was established. This expert system takes advantage of both a neural network and a...By combining the artificial neural network with the rule reasoning expert system, an expert diagnosing system for a rotation mechanism was established. This expert system takes advantage of both a neural network and a rule reasoning expert system; it can also make use of all kinds of knowledge in the repository to diagnose the fault with the positive and negative mixing reasoning mode. The binary system was adopted to denote all kinds of fault in a rotation mechanism. The neural networks were trained with a random parallel algorithm (Alopex). The expert system overcomes the self learning difficulty of the rule reasoning expert system and the shortcoming of poor system control of the neural network. The expert system developed in this paper has powerful diagnosing ability.展开更多
文摘Due to the competition and high cost associated with die casting defects, it is urgent to adopt a rapid and effective method for defect analysis. In this research, a novel expert network approach was proposed to avoid some disadvantages of rule-based expert system. The main objective of the system is to assist die casting engineer in identifying defect, determining the probable causes of defect and proposing remedies to eliminate the defect. 14 common die casting defects could be identified quickly by expert system on the basis of their characteristics. BP neural network in combination with expert system was applied to map the complex relationship between causes and defects, and further explained the cause determination process. Cause determination gives due consideration to practical process conditions. Finally, corrective measures were recommended to eliminate the defect and implemented in the sequence of difficulty.
基金This work was supported by the project 863 ofChina(No.863-511092)
文摘The coagulation process is one of the most important stages in water treatment plant, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, PH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Based on neural network and rule models, an expert system for determining the optimum chemical dosage rate is developed and used in a water treatment work, and the results of actual runs show that in the condition of satisfying the demand of drinking water quality, the usage of coagulant is lowered.
文摘In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.
文摘In this paper, we propose a formal definition, general structure and work principle of the Neural Network Expert System (NNES) based on joint-type knowledge representation, and show a practical application example using NNES for forecasting the water invasion of coal mine.
文摘The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrations was proposed, which uses neural networks, rule models and a single loop control scheme. First, the process was described and the strategy that features an expert controller and three single loop controllers was explained. Next, neural networks and rule models were constructed based on statistical data and empirical knowledge on the process. Then, the expert controller for determining the optimal concentrations was designed through a combination of the neural networks and rule models. The three single loop controllers used the PI algorithm to track the optimal concentrations. Finally, the implementation of the proposed strategy were presented. The run results show that the strategy provides not only high purity metallic zinc, but also significant economic benefits.
文摘Using expert systems in intelligent CAD of electrical machines have limitations such as knowledge acquisition bottlenecks and matching conflict, combinatorial explosion, and endless recursion in the reasoning process. This paper discusses the principle of a hybrid system of a neural network and an expert system (HNNES), i.e., knowledge representation, reasoning mechanism, and knowledge acquisition based on neural networks. An architecture of HNNES is presented in consideration of the feature of the design of electrical machines.
文摘In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.
文摘By combining the artificial neural network with the rule reasoning expert system, an expert diagnosing system for a rotation mechanism was established. This expert system takes advantage of both a neural network and a rule reasoning expert system; it can also make use of all kinds of knowledge in the repository to diagnose the fault with the positive and negative mixing reasoning mode. The binary system was adopted to denote all kinds of fault in a rotation mechanism. The neural networks were trained with a random parallel algorithm (Alopex). The expert system overcomes the self learning difficulty of the rule reasoning expert system and the shortcoming of poor system control of the neural network. The expert system developed in this paper has powerful diagnosing ability.
