Cost Estimation of Transmission Line Based on Artificial Neural Network

Based on the structure of cost factor in transmission lines project and the characteristic of neural networks, the paper shows up using artificial neural networks to estimate the cost of project and provide a method to investigate them. By analyzing the structure of project cost and influence factor, the paper builds both input and output neural cell for cost of transmission line project. The paper builds the model of artificial neural networks and exports the steps of the algorithmic. The project sample is used in history to train and test the model. At last, the model gets satisfactory result and meets the investigation requirement of engineering budgetary estimation. Therefore, the paper provides an impersonality and quick investigation method for budgetary estimation of power projects.     

基于人工神经网络的植物油料实际压缩比与临界压榨压力预测

基于BP神经网络建立了菜籽、菜籽仁、花生、大豆、芝麻和亚麻籽的实际压缩比预测模型,实际压缩比神经网络预测值与试验实测值吻合。根据预测的实际压缩比曲线,确定出油料工程实际临界压榨压力分别为菜籽、菜籽仁、花生、亚麻籽80MPa,芝麻100MPa,大豆60MPa。     

Applications of the Fuzzy Neural Networks to Short term Load Forecasting

This paper presents a fuzzy neural network approach to short term load forecasting.It can predict the hourly loads for next day or next week with the fuzzy information.The practical examples have proved the efficiencies of the proposed approach.     

Fault Diagnosis in Hydraulic Turbine Governor Based on BP Artificial Neural Network

The governing system of hydraulic turbine generator plays an important role in power system. It is significant to find out the faults of governing system and remove them quickly. This paper sets up a new fault diagnosis model of the hydraulic turbine generator governing system with the advanced ANN (artificial neural net). This 17-in-13-out model consists of three layers. It is proved that this model can find the fault accurately.     

Predictive Control Approach for Active Power Filter Based on Neural Networks

In this paper, a predictive control approach using neural networks for active power filter is proposed. The control system of active power filter using this method make using of the internal model control technology based on neural networks, meanwhile, to solve its questions such as lag because of calculating using neural networks, a predictive model based on neural networks is introduced. Simulation analysis shows that this control approach can compensate the lag of system and take advantage of self-adaptive characteristic of neural networks. Good result can be obtained.     

A Kind of Fuzzy System Modelling and Control Based on Neural Networks

Combining artificial neural networks(ANN) with fuzzy system theory,a kind of modelling & control method of fuzzy system based on ANN is presented.The simulation researches have verified that the proposed approach can be applied effectively to a number of control systems which are defficult to build strict mathematical model.     

Artificial Neural Network Fault Diagnosis System Based on Rough Set Theory

On the basis of fault diagnosis neural network model, in this paper, knowledge representation system of rough set theory is taken as a major tool to delaminate the complex neural network and in which unnecessary properties are eliminated. This method overcomes some shortcomings, such as network scale is too large and the rate of classification is slow. The good effect that reduces the matching quantity of pattern search in classification course is gotten. The structure and algorithm of layered-mining neural network model based on rough set theory are also given. The example shows that this system has higher reference value in practical application.     

Dynamic Control Model for the Secondary Cooling of Slab Casting Based on Neural Networks

A dynamic control model for the secondary cooling of slab casting is presented to reduce the difference between the actual temperature and the goal surface temperature of slab. The model, which is based on the BP neural networks for forecasting the temperature and the fuzzy neural networks for dynamically controlling the water in the secondary cooling in the continuous casting, could timely adjust and allocate the water according to the speed and temperature of slab. A series of tests have been conducted based on inputs of the No. 2 slab caster in a steel plant. It has been shown that the model, which integrate the charateristics of water controlling problem in secondary cooling into the temperature status of slab during the cooling process, can control the water in secondary cooling efficiently and dynamically according to the situation of actual production.     

Evaluation Method Based on Artificial Neural Network in Contractor Prequalification of Tenderers

Both tenderees and tenderers attach much importance to contractor prequalification before bidding because it plays important roles for both and so far the Score Method has been widely used in our country. But the weights in the method are man-made, which may be affected by personality inevitably. In order to avoid this influence, this thesis will introduce another method based on artificial neural network. This thesis expatiates the principle of artificial neural network, and analyzes the characters of prequalification. Then, the author sets up the mathematics model. In the end, the author analyzes this method by an example.     

基于人工神经网络的落叶松毛虫发生量预测模型的研究

运用人工神经网络的原理和算法,根据相关系数法和逐步回归法选取了蒸发量、气温、风速、相对湿度等气象因子作为预报因子,建立了内蒙古东部地区的鄂伦春自治旗落叶松毛虫的发生面积及虫口密度与气象因子之间的BP网络模型。结果表明：所建立的模型具有较高的预测效果。通过逐步回归筛选出的预报因子,与事实吻合,选取合理。误差较小,控制在0.1%~25.0%之间。可以作为病虫害综合防治的依据。     

Artificial Neural Networks Models for Flow Stress during High Temperature Plastic Deformation of Al-Li Alloy

Using gained experimental data to develop the models of stable flow stresses at high temperature plastic deformation by statistical methods for alloy materials, precision of the models is poor and at the same time the processes of modeling are complicated with great workload. On the basis of the data obtained on Gleeble-1500 Thermal Simulator,the predicting models for the relation between stable flow stress during high temperature plastic deformation and deformation strain, strain rate and temperature for 1420 Al-Li alloy have been developed with BP Artificial Neural Network method. The results show that the model on basis of BPNN is practical and it reflects the real feature of the deforming process. It states that the difference between the real value and the output of the model is in order of 5 percent.     

Vacuum Circuit Breaker''s Condition Simulation Based on Artificial Neural Network and Electrical Endurance Quality Criterion

How to select the factors that have more great affection on the condition based maintenance and how to make sure the relation among them are two key problems. A new method's proposed for vacuum circuit breaker's condition based maintenance, which combines electrical endurance quality criterions and condition recognition arithmetic based on artificial neural network. The paper proposes a model based on electrical-endurance quality criterions by applying ANN in vacuum circuit breaker's condition based maintenance. The simulation indicates that the criterion selected in this way are reasonable and the network with new learning error function has a much better generalization ability.     

PID Control for Multivariable System Based on Ameliorative RBF Neural Networks

Aiming at systems which are of characteristics of multi-input and multi-output, nonlinearity and time-variation in the industrial control fields, this paper presents a intelligent PID control method based on ameliorative RBF neural networks, which constructs RBF neural networks identifier on-line and identifies a controlled object on-line by means of adopting the nearest neighbor-clustering algorithm, and adjusts parameters of PID controller on-line and realizes decoupiing control of multivariable, nonlinear and time-variation system. The simulation result indicates that the controller can get parameters which are optimal under some control law, it makes the decoupled system, compared to the PID control method based on the conventional RBF neural networks, has perfect dynamic and static performances, possesses the advantages of high precision, quick response speed and is of great adaptability and robustness.     

Application of Artificial Neural Network in Modeling Paint Waste Water Treated by Coagulation Oxidation Process

An artificial neural network (ANN) model was established based on data of paint waste water treated by coagulation oxidation process, using the improved back propagation algorithm. The model was then used to fit and predict some experimental data. The results indicated that the errors between computed data and experimental data were much small. Furthermore, the ANN model could correctly reflect the mechanism of some factors which affected the efficiency of paint waste water treatment.     

A Study on Artificial Neural Networks-based Medical Equipment Demand Forecast

In order to uncover the complex relation in the inventory data and lower the inventory level, based on the idea of black box method, all factors that influence demand are identified firstly. Then an initial forecast model of BP neural networks that adopt LM algorithm is established, and is trained using hospital inventory's history data. The interrelation's information of each factor that influences demand is stored in the link weights matrix W dispersedly. The final forecast model is obtained. We use the model to forecast the demand of medical equipment in Daping hospital. Based on it, the inventory cost is reduced enormously. The theory for the inventory system can be used to make management decision.     

GA Based on Structural Formula Binary-coding and Its Applications in BP Networks

According to the topological characteristics of BP networks, a Genetic Algorithm based on the structural formula binary-coding has been designed in this paper. By means of fractionalizing the large-scale solution-space and performing the GA operations to the fractionalized subspaces, the GA's global-convergence and parallelism can be utilized to search the subspace for the optimal solution in the whole solution-space, thus definitude the starting point and narrow the domain for the next BP's local-search. Testing shows that the two-step algorithm (GA-BP) can solve the existed problems in the NN's training such as local minimum, tardy convergence and so on.     

Survey on Stability, Bifurcation and Chaos in Delayed Neural Networks

From the viewpoint of nonlinear dynamics, this review outlines the recent advances as well as some open problems in the study of neural networks with time delays, an important class of delayed systems in various neural network models. The survey includes three aspects as fellows: the dynamic features, available approaches and advances in research on most attractive problems. The evolution of a delayed neural network depends not only on the current state of the systems but also on previous ones. Hence, a delayed neural network should be modeled by a functional differential equation, the solution space of which is of infinite dimensions. Therefore, the dynamical behavior of delayed neural networks is very complex.