Application of BP Neural Networks in Inventory Dynamic Modeling

The authors establish an original inventory model based on BP neural networks in dynamic environment using the inventory history data, and overcome the net overfitting problem occurred with insufficient samples by using early stopping method. After a simple-relation inventory model training is attained, they get inventory model and analyze it dynamically by reconstructing the model after getting new sample data. An example is provided to illustrate the model. The theoretical evidence is provided for the inventory system to make management decision.     

Improved Ellipsoidal unit Neural Networks and Its Applications in CSTR

To overcome the limitations of the standard ellipsoidal unit neural networks, some new approaches used in ellipsoidal unit neural networks have been proposed. These new approaches address three main issues: firstly, to understand better and represent the nature of fault classification boundaries; secondly, to determine the network structure without the usual trial and error schemes; lastly, to avoid erroneous generalizations. The application in CSTR shows that the ellipsoidal unit networks can possess arbitrary nonlinear classifying ability, nonlinear interfacial describing ability, and obtain accurate and efficient diagnosis results.     

The Application of Fuzzy Neural Networks for Power Transformer Fault Diagnosis

This paper presents the application of fuzzy neural networks (FNN) in power transformer faults diagnosis.A FNN model is builded,in which input is transformer oil color spectrum analysis and output is fault type.The test results of fault examples show its effectiveness and potential applicable worthiness.     

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.     

Forecast of the Horizontal Displacement of Cement-soil Gravity Retaining Wall Using BP Neural Networks

The authors are the first who succeeds in using BP neural networks to forecast the horizontal displacement of cement soil gravity retaining wall. This method is proved to be very useful and important. It has not only the ability to resolve many very complex problems, which cannot be solved by numerical methods, but also accords very well with the spot measurement values.     

Multiple Layer BP Neural Network for Fuzzy Calculating

The FCBP(Fuzzy calculating BP) algorithm which is proposed by this paper hasovercome the sensitivity for samples,reduced the number of input layer's samples,lightened the burden of input layer.it is suitable to fuzzy inference and pattern recognition.     

Computer-aided Diagnosis of Breast Cancer Based on Support Vector Machine

Combined with the breast fine needle aspiration cytology,the SVM,K-Nearest Neighbor(K-NN) and Probabilistic Neural Network(PNN) are used to diagnose the breast cancer.The best overall accuracy reaches 96.24% via SVM with Sigmoid kernel by using 5-fold cross validation,and is superior to those of other classifiers including K-NN(95.37%) and PNN(95.09%).Support vector machine is capable of being used as a potential application tool for SVM-aided clinical breast cancer diagnosis.     

BP神经网络在2005年太湖蓝藻水华预警中的应用

蓝藻水华是目前中国乃至世界面临的重大环境问题之一。为了有效地减少及预防蓝藻水华带来的影响,收集苏州市吴县1986—2007年的气象资料,运用主成分分析法,分析了太湖蓝藻暴发前一个月的主要限制因子及其相互关系。气温、气压、相对湿度、降水是影响叶绿素a浓度的主要限制因子。结合2005年1—10月太湖各区域蓝藻叶绿素a浓度的含量,利用Matlab R2010a软件,建立了基于BP神经网络的蓝藻水华预警模型,可为采取相应措施和控制蓝藻水华提供科学依据。     

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.     

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.     

基于逐步回归与BP神经网络的日光温室温湿度预测模型对比分析

为构建较准确的日光温室温湿度预测模型,于2011-2013年冬季(1月、2月、12月)天津市宝坻区开展温室内外环境监测试验,并建立3种天气类型(晴、多云、阴)下3个时段(0-8时、8-17时、17-23时)逐步回归与BP神经网络温室内温湿度预测模型。结果表明:1)温室内气温逐步回归模型9种情况下模拟值与实际值的绝对误差小于3℃的平均准确率Rate(≤3℃)为88%,平均均方根误差(RMSE)为2℃;BP神经网络模型9种情况下模拟值与实际值的绝对误差小于3℃的平均准确率Rate(≤3℃)为94%,平均均方根误差(RMSE)为1.6℃。应用BP神经网络建立的气温预测模型相对更为准确稳定。2) 相对湿度逐步回归模型9种情况下模拟值与实际值的绝对误差小于6%的平均准确率Rate(≤6%)为81%,平均均方根误差(RMSE)为5.7%;BP神经网络模型9种情况下模拟值与实际值的绝对误差小于6%的平均准确率Rate(≤6%)为80%,平均均方根误差(RMSE)为6.7%。两类模型均不适宜预测8-17时日光温室相对湿度,而17-23时与0-8时应用逐步回归建立的湿度预测模型相对更准确稳定。     

Creating FT-IR Spectroscopy Recognizable Patterns for Lung Cancer Tissue

Fourier Transform Infrared (FT-IR) spectrum is an effective method of investigating substance molecular structure swiftly while keeping the specimens intact, so it can represent the characteristic of the cancer at molecular level. Then all the differences of spectra between cancerous tissues and normal tissues are determined with FT-IR spectroscopy and analyzed with statistics of Logistic regression, the results show the pattern can be distinguished to lung cancerous tissues from normal tissues, and it is feasible to apply FT-IR spectroscopy in clinical practice.     

The Three-Valued PMC Model Computer System Diagnosis Implemented by Artificial Neural Network

This paper systematically studied how to construct a neural network to realize the three-valued computer system diagnosis problems. Through a lot of simulation it seems effective to solve the computer system diagnosis problems with the help of network. Meanwhile the simulation also reveals the relations among capacity C, resistance R , gain Kand time.     

BP神经网络在烟蚜发生程度预测中的应用

为实现对烟田烟蚜发生程度的预测预报,以12年的历史资料为基础数据,采用BP神经网络方法建立了烟蚜发生程度的预测模型。该模型对待测样本的预测准确度为99.43%,回测准确度为87.36%。所建立的预测模型可提前1个多月对烟蚜发生程度进行预测,为中期预测模型,其预测结果可为烟田蚜虫综合治理提供依据。     

Application of fuzzy Neural Networks to Evaluation of Transversal Economic Benefits

A new method for evaluation of transversal economic benefits is researched by fuzzy neural networks, BP algorithm is used to learn the connection weights of the fuzzy neural networks and partitions of fuzzy subsets. It has been shown by the modeling and evaluating results about the economic benefit index system of ten enterprises that the method has reinforcement learning properties and universalized capabilities. with respect to modeling and evaluating of nonlinear systems which have some uncertainties, the method is available.     

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。     

Application of Intelligent Neural Network on Prediction of Time Series Signal

The research of neural network has been maturated both in theory and practical application since 1980's, and also been employed into the prediction and analysis of nonlinear time series signal in the field of signal process system. Concerning with the problem of time series signal prediction based on traditional neural network, such as black box, poor accuracy, and facing the shortage of post knowledge, this paper presents a different neural network prediction model from the traditional ones, based on intelligent neural cell model and employing the iterative prediction method. Through the example on stock price prediction, the prediction accuracy and practical value are proved.     

遗传算法优化人工神经网络模型在日光温室湿度预报中的应用

提出一种基于遗传算法优化BP神经网络的方法预测日光温室湿度环境因子。实测日光温室内影响空气湿度的环境因子组成数据样本作为神经网络的输入,采用基于实数编码的遗传算法替代随机设定神经网络的初始权阈值,然后通过改进的BP算法在由遗传算法确定的搜索空间中对网络进行精确训练。模型预报值和实测值基于1：1线的决定系数R2和预测平均相对误差MSE分别为0.9857和3．1％。结果表明,遗传算法优化BP神经网络预报模型收敛速度快、预测精度高。可为日光温室的湿度环境调控制提供理论依据和决策支持。