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.     

Evaluation of Academic Degrees and Graduate Education Based on Neural Network

With the development of the academic degrees and graduate education, evaluation is the important measure for guaranteeing the quality in the area. With increasing complication of the target of the evaluation of academic degrees and graduate education , it is necessary to introduce nonlinear method of the evaluation, which has great significance for the validity of the result of the evaluation. Neural networks have been developed greatly in recent years,they have the advantages of nonlinear mapping,parallelity ,adaption, etc. Therefore , they are good tools of nonlinear modeling. On the basis of setting up the model of the evaluation based on neural network ,this paper presets the practical value of the method.based them.     

基于BP神经网络的农业电子商务网站评价研究

提出了适合农业电子商务网站的评价指标体系。建立农业电子商务网站的BP神经网络评价模型,通过BP神经网络对数据进行训练、测试,对提出的评价指标体系进行了验证。通过对结果的分析发现,建立的基于BP神经网络的评价模型在农业电子商务网站评价中具有可行性,在网站建设方面具有较强的参考价值。     

Improved Learning Effect Synthetic Evaluation Method Based on Backpropagation Neural Network

Aim at the practicability issue,study effect-evaluating model based on B-P NN was improved on evaluation standards,evaluation model and training swatch.Evaluation standard effectivity was added to set the conversion function of network crytic-layer nodes as'tansig' and that of output-layer nodes as'logsig'.The taining swatch was improved.The simulation results validated the evaluation veracity of the model.     

BP人工神经网络在土地集约利用评价中的应用研究——以浙江省遂昌工业园区为例

为了探索土地集约利用评价的新途径,笔者尝试运用BP人工神经网络模型及实证分析的方法对浙江省遂昌工业园区的土地集约利用水平进行评价。研究结果表明,遂昌工业园区的土地集约利用程度在浙江省处于中下水平,导致集约度偏低的主要原因在于土地建成率不高,工业用地产出强度和固定资产投入强度偏低。并得出以下研究结论,BP人工神经网络体现了土地集约利用评价的科学性与合理性,能很大程度上避免人为因素对评价结果的影响,具有很强的实际应用价值。     

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.     

土壤含水率与像素颜色之间关系的BP人工神经网络模型

建立像素颜色RGB值与土壤含水率之间的数学关系,是染色入渗法的应用基础。结合沟灌染色入渗试验。研究了染色入渗过程中土壤含水率与像素颜色分量之间关系的BP人工神经网络模型。分析土壤含水率与像素颜色分量之间的关系,确定BP人工神经网络的拓扑结构,以像素颜色分量的相对值作为输入因子。土壤含水率作为输出因子,建立了包含1个隐层的BP人工神经网络。结果表明,该模型具有较高的拟合精度和验证精度,优于二次多项式模型。     

Polynomial Feedforward Artificial Neural Network

A new artificial neural network,i.e.,polynomial feedforward artificial neural network(PFANN), which has three layers(input layer,hidden layer and output layer) is presented. The neural activation functions of hidden layer and output layer are f(x)=x p and linear function, respectively. The learning method of hidden-output layer weights is the steepest descent method and the one of input-hidden layer weights is genetic algorithm(GA) . During the learning process, the error function is decreased monotonely. So the learning algorithm is convergent and the network ,which can approximate to arbitrary continuous function , is stable. Some applying samples of PFANN, which reveales the remarkable quality, are proposed,too.     

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。     

Research and Development of Evaluation system Based on Neural Network and Expert System

As a single system of evaluation, both neural network (NN) and traditional expert system (ES) have many limitations. Developing evaluation system integrating NN with ES is an available way to offset their weakness. With black-box module, a new frame of evaluation system is developed in relative independent method. Then, a way of programming and realizing evaluation system, which is more close to human thinking, is put forward.     

离差最大化结合BP神经网络评价烟叶化学品质

为解决烟叶化学品质现有组合评价方法的不足,探讨离差最大化组合评价法与BP神经网络相结合进行评价。选取4种典型单一评价法,首先采用改进熵权法和AHP法确定指标权重,然后根据离差最大化原理计算组合评价值,最后利用BP神经网络对组合评价值进行反演。结果表明,离差最大化组合评价值与单一评价法相关性较其他组合评价法更高,平均相关系数为0.9822;BP神经网络对组合评价值有较高的预测准确性与稳定性,预测值与实际值相对误差不超过3%,决定系数大于0.9900。说明,离差最大化组合评价法对单一评价法的组合效果更好,BP神经网络提高了组合评价的便捷性。     

The Study on the Branched Feedforward Neural Network

Based on the BP algorithm,the Branched Feedforward Neural Network,one ofthe artificial neural network medel used for pattern classification is proposed in this paper, and therelated algorithm is also offered here, Several typical examples of pattern classification are studiedwith computer simulation, The simulation results show that the training time of Branched Feedforward Neural Network is obviously reduced and the classifying effect is much better as compared withgeneral BP Network.     

Design and Application of Compensatory Fuzzy Neural Network Based on Adaptive Learning Rate Method

To overcome the disadvantages of general networks such as slow convergence speed and being unable to combine with the expert knowledge etc,the authors introduces compensatory fuzzy neural cells,integrate the powerful knowledge expressiveness of fuzzy system and the excellent self-learning of neural network,and present a novel Compensatory Fuzzy Neural Network(CFNN) based on Adaptive Learning Rate Method which changes the learning rate using Adaptive Learning Rate Method in dynamic way.Finally this method is applied to the actual case.The result proves that it not(only) can adjust parameters properly on line,but also can optimize relevant fuzzy reasoning in dynamic way,fasten training rate.     

基于模糊数学与神经网络的农业产业集群评价研究

针对目前农业产业集群评价研究现状较少状况,尝试运用模糊数学方法对指标数据运用隶属度函数处理,以一级模糊综合评价向量作为神经网络评价模型的输入,再运用神经网络模型对农业产业集群进行评价,神经网络的非线性处理能力使农业产业集群评价更加科学化,并以连云港农业产业集群评价为例给出了实证研究。     

An Artificial Neural Network Circuit Model for Routing Communication Traffic

A new fluid artificial neural network circuit model for routing communication traffic is proposed.The features of the model are that it can make use of all topology information of the network,and the optimization calculation is performed with electric circuit,and the most optimum route for communication traffic in the network can be obtained in circuit reaction time.Simulation results indicate that the model is correct and feasible. [WT5HZ]     

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.     

Malfunction Diagnosis of Three-phase Full-bridge Controlled Rectifier Based on BP Neural Network

The diagnosis of the power electronic circuit is very intricate. One of the main reasons is that the structure of the circuit will change if the power device is not working .The thyristor is the easiest to be mangled. So diagnosing the malfunction is the most important about the diagnosis of the power electronic circuit. The paper puts forward a malfunction diagnosis of the thyristors of three-phase full-bridge controlled rectifier with BP neural network. After analyzing the output waveforms of malfunctioning circuit and training a BP neural network with the sampling data of malfunctioning waveforms, a well training BP neural network is constructed and used to diagnose the malfunction. The simulation and experiment demonstrate that this method is valid.     

The Method of Diagnosing Brain-dysfunction Based on Membership Function and BP Neural Network

Membership functions.formed with the characteristic values obtained by applying the continuous reaction time (CRT) theory from controls and heptic encephalopthy and proposedin this paper.whith these membership functions,the CRT data to be diagnosed are calculated in advance,and then discriminated by BP meural network to differentiate patients with or without braindysfunction.The troubles of low accuracy and efficiency,encountered in training BP network withfuzzy samples.scattered data and extended samples,are solved.