Application of an expert system using neural network to control the coagulant dosing in water treatment plant

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.

MOLTEN SALT PHASE DIAGRAMS CALCULATION USING ARTIFICIAL NEURAL NETWORK OR PATTERN RECOGNITION-BOND PARAMETERS PART 3.ESTIMATION OF LIQUIDUS TEMPERATURE AND EXPERT SYSTEM

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The Principle and Architecture of a Hybrid System of a Neural Network and an Expert System in Intelligent CAD of Electrical Machines

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.

Expert Diagnosing System for a Rotation Mechanism Based on a Neural Network

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.

ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

Nerual Network Expert System and Their Application to Forecasting Water Invasion of Colliery

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.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

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.

Expert Network for Die Casing Defect Analysis

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.

A NOVEL INTRUSION DETECTION MODE BASED ON UNDERSTANDABLE NEURAL NETWORK TREES

Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.

A STUDY OF EXPERT SYSTEM FOR SECTION EXTRUSION PROCESS BASED ON FINITE ELEMENT METHOD SIMULATION

The samples obtained by Finite Element Method (FEM) simulation for section extrusion process have been trained on BP Neural Networks. The mapping relationsbetween die's geometrical parameters and energetic parameters, such as stress and strain generated in the die are established. The extrusion process model and its expert system are also determined. The excellent expansibility this system possesses provides a new prospect for the future development of expert system for section extrusion dies.

Study on Missile Intelligent Fault Diagnosis System Based on Fuzzy NN Expert System

In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuzzy NN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, the intelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment. The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosis for large-scale missile weapon equipment.

The FAM(Fuzzy Asociative Memory)neural network model and its application in earthquake prediction

FAM(Fuzzy Associative Memory) Network Model, FAM Adaptive Learning Algorithm and Principal of FAM Inference Machine are introduced, and successfully application to ″New Generation Expert System for Earthquake Prediction″ (NGESEP). This system has good function for knowledge learning without disadvantages of neural network, which the learned knowledge implied in network is difficult to be understood or interpreted by expert system.

Intrusion Detection Approach Using Connectionist Expert System

In order to improve the detection efficiency of rule-based expert systems, an intrusion detection approach using connectionist expert system is proposed. The approach converts the AND/OR nodes into the corresponding neurons, adopts the three layered feed forward network with full interconnection between layers, translates the feature values into the continuous values belong to the interval [0, 1], shows the confidence degree about intrusion detection rules using the weight values of the neural networks and makes uncertain inference with sigmoid function. Compared with the rule based expert system, the neural network expert system improves the inference efficiency.

RESEARCH ON EXPERT SYSTEM OF FAULT DETECTION AND DIAGNOSING FOR PNEUMATIC SYSTEM OF AUTOMATIC PRODUCTION LINE

Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.

An Expert System for the Prediction of Surface Finish in Turning Process

Prediction of surface finish in turning process is a difficult but important task. Artificial Neural Networks (ANN) can reliably pred ict the surface finish but require a lot of training data. To overcome this prob lem, an expert system approach is proposed, wherein it will be possible to predi ct the surface finish from limited experiments. The expert system contains a kno wledge base prepared from machining data handbooks and number of experiments con ducted by turning steel rods, over a wide range of cutting parameters. With this knowledge base, the expert system predicts surface finish for different tool-w ork-piece combinations, by carrying out few experiments for each case. The prop osed expert system model is validated by carrying out a number of experiments.

Artificial Neural Network Applied to Quality Diagnosis

In this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in the area of product quality diagnosis, prediction and control, state supervision and classification, factor recognition, and expert system based diagnosis, then set up the ANN models and expert system for quality forecasting, monitoring and diagnosing. We point out that combining ANN with other techniques will have the broad development and application of perspectives. Finally, the paper gives out some practical applications for the models and the system.

Design and Application of an Expert System for Equipment Maintenance and Forecast

The maintenance and forecast expert system of equipment based on Artificial Neural Network is composed of control, measure, failure forecast, execution, data processing module and database. The data processing module obtains the change of the controlled objects＇ structure and parameters, then takes correspondent measures according to the examination and diagnosis information. The failure forecast module finds the control system fault, separates the fault symptom location, tells the fault kind, estimates the magnitude and time of the fault, and finally makes evaluation and decision.

Study of Properties of Intermetallic Compounds of Rare Earth Metals by Artificial Neural Networks

The results of an expert system of lanthanide intermetallic compounds using artificial neural networks and chemical bond parameter method were reported. Two pattern recognition neural models, one for prediction of the occurrence of 1 : 1 lanthanide intermetallic compounds with CsClstructure and the other for prediction of congruent or incongruent melting types, were developed. Four regression neural models were also developed for prediction of melting point of these compounds. In order to get rid of overfitting, cross-vahdation method was used for the neural models. And satisfactory results were obtained in all of the neural models in this paper.

AN EXPLOITATION OF THE EXPERT SYSTEM OF MECHANICAL DESIGN BASED ON VIRTUAL REALITY

Object oriented methodology is introduced to build the model on expert system of mechanical design. The combination of knowledge representation with object oriented method and neural network make the expression of the complex knowledge in mechanical design world reasonably and properly, and the complicated system is built successfully. Also, the merging of virtual reality art and conventional mechanical design theory puts visual three dimensional mechanical movement into realities.

Intelligent Decision Support System for Bank Loans Risk Classification

Intelligent Decision Support System (IISS) for Bank Loans Risk Classification (BLRC), based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BLRC, the key financial and non-financial factors are analyzed. Meanwhile, ES and Model Base (MB) which contain ANN are designed . The general framework,interaction and integration of the system are given. In addition, how the system realizes BLRC is elucidated in detail.