基于主成分分析的BP神经网络估算藏北高寒草地覆盖度变化

藏北高寒牧区草地是中国高寒草地分布面积最大的地区。为了及时准确地获得该区域草地覆盖度的变化趋势,本研究利用多年气象数据、社会统计数据、GIMMS、MODIS两种归一化植被指数（NDVI）数据作为参数,构建 BP神经网络模型,估算2010—2014年藏北高寒草地年际变化趋势,并用主成分分析方法优化参数来改进模型。结果表明,① BP神经网络模型及其改进模型对藏北高寒草地覆盖度年际变化趋势与遥感值的相关系数为0.16、0.47,表明通过主成分分析优化参数后的BP神经网络模型具有较好的模拟效果。 ②两种BP神经网络估算的植被指数值与NDVI值平均误差率分别为2.36%、2.20%。均有较高的模拟精度。③从神经网络训练步数上看,BP神经网络结果训练收敛步长为5000,基于主成分分析的BP神经网络模型训练收敛步长为454,表明后者提高了计算效率,体现出良好的收敛性。因此,无论从年际变化趋势拟合程度、植被指数估算值精度、还是从计算效率来看,改进的BP神经网络模型对于估算藏北高寒草地覆盖度变化更加行之有效。     

Applications of SOM Neural Network in Multiple-faults Diagnosis of Turbogenerator Set

The turbogenerator vibration faults have the character of variety. Many faults often occur synchronously. The traditional BP neural network can diagnose the single fault effectively. If we diagnose the multiple faults by using the BP neural network, we must train all samples of multiple faults, which is will increase the number of training samples and the burden of learning greatly. So the diagnosis can not be performed easily. This paper introduces a method based on SOM neural network, which is studied by using the single sample and diagnosing the multiple faults according to the position of output nerve cell. By analyzing the examples, the method is proved to be available for diagnosing the multiple faults of Turbogenerator set.     

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。     

The Fuzzy Inputing And Outputing Method in Vibration Fault Diagnosis of Steam Turbine-Generator Set

A fuzzy neural network diagnosis model is established on the basis of the vibration failure features of steam turbine-gernerator set, two kinds of fuzzy inputing method are discussed. At last, the performance of the fuzzy neural network is compared with that of the conventional BP network. The results show that the method presented is suitable for identifying the vibration failure of steam turbine-gernerator set, and it is more efficient in deal with the uncertain data than BP network diagnosis.     

Analysis on the Control of Variable Speed Wind Turbine Using Neural Network Controller

It is very difficult to build the accurate mathematical model of the wind turbine generator system because of the uncertainty of air kinetics and the complexity of power electronics, especially when the wind speed changes abruptly or there is a disturbance. But the classical control needs the model. Using neural network controller to the wind turbine generator system can overcome these difficulties. The wind speed can be followed and the maximum power can be obtained under low wind speed by using the power coefficient curve BP neural network and the optimum pitch angle BP neural network. The maximum power can be kept and under the allowed range in the condition of high wind speed. The simulation model and result are given under the environment of MATLAB. The fluctuation of wind speed can be controlled and the disturbance can be cancelled by BP neural network controller.     

Self-adaptation Optimize BP Neural Network Design Based on the Genetic Algorithms

BP(Back Propagation) Neural networks is in the presence of the local optimization in the Neural networks training.The algorithm have slow convergence and the local convergence problem which impact the neural networks work performance.In order to cover these shortcomings and solves the size's hugeness and the low efficiency of the net problem in the traditional NN designing,the action principles of BP-Neural network's structure are analyzed,and a new method is formed which is confirmed from the Enhance genetic algorithms(EGA).The method can identify network configuration and network training methods.By adopting the number coding,self-adaptable multi-point variations operation,this method can effectively reduce the network size and the network convergence time,increase the network training speed.Tomatoes disease diagnosis examples illustrate the feasibility of this approach.     

THE NEURAL NETWORK CONROL TECHNOLOGY USED IN HYDRAULIC LOGIC VALVE SYSTEM

This paper is concerned with artifical neural network used in hydrauic logic valve control technique, the characters and structure of hydraulic logic valve, the main characters and topological construction of artifical neural network are analyzed in detail. The pre-feedback used in logic valve. is focused, the training method was derived and improved. Using this improved BP training method, the net work was trained and satisfactory experiment results were gained.     

基于LM-BP神经网络的耕地土壤养分等级划分模型──以皖南山区为例

研究旨在通过BP神经网络方法,构建起LM-BP网络结构(5-M-1)模型,达到对土壤养分等级划分的目的,为合理的土壤养分管理提供可靠依据。采用Levenberg-Marquardt (LM)训练算法,构建3层网络模型：一个输入层、一个隐含层、一个输出层,利用3层网络作为耕地土壤养分等级划分模型。利用土壤养分各级评价标准作为模型的训练样本和测试样本,以此来对BP神经网络进行训练和测试,并对歙县土壤养分进行综合评价。结果表明：LM-BP网络结构对测试样本输出的预测值和实际参考值是一致的。最终通过灰色关联模型和主成分分析方法对歙县土壤养分的综合评价结果与BP神经网络的模拟结果相对比,发现也是基本一致的。LM-BP网络结构应用于土壤养分等级划分中,得到了很好的预测效果,为智能算法应用于农业领域奠定了良好的基础。     

Nonlinear Forecast System for River Water Pollution Based on GIS

Based on the analysis of the water pollution spatial distribution characters of Yangtze River in Chongqing,a new method based on the integration of BP neural network and genetic arithmetic(GA) is proposed.For some shortcomings existed in the standard BP neural network,this method has ultimately overcome these shortcomings by combining the GA with BP artificial neural network through altering stimulating function,adding momentum factor to power value for BP algorithm and introducing genetic arithmetic to searching for the knots of the hidden layer,momentum factor and learning level.Using this method can easily overcome the difficulty of measuring the water prediction model's parameters.GIS is used as a tool for data management and spatial analysis,and the prediction result of the model for the water pollution spatial distribution characters of Yangtze River in Chongqing is visualized and explored with the precision of more than 78%.     

基于遗传算法的BP神经网络在小麦蛋白质含量预测中的应用

为了快速、简便、准确地测定小麦蛋白质的含量,本文提出了应用近红外光谱分析技术结合遗传算法(GA)的BP神经网络的建模方法。采用SPXY算法对光谱数据进行了合理划分,并运用连续投影算法(SPA)将预处理过的数据压缩,对光谱数据提取最佳敏感波点作为GA-BP神经网络的输入,建立小麦蛋白质含量的校正模型。模型的预测均方根误差和预测相关系数为1.3379和0.979,并与BP神经网络所建立的校正模型进行了比较。结果表明：GA-BP神经网络所建模型收敛速度快、训练时间短、准确度也较高,能够实现对小麦蛋白质含量快速高效的检测。     

雄先型核桃雄花疏除的二次回归与BP神经网络模型研究

雄先型核桃雄花疏除（去雄）是提高产量的重要管理措施,为提高核桃去雄的效率,建立二次回归与BP神经网络模型。分别以乙烯利、赤霉素和甲哌鎓为自变量和核桃雄花脱落率为响应指标,进行田间建模试验,建立了二次多项式回归方程和BP神经网络模型,并于翌年进行BP模型田间确认试验。试验数据分为训练集、确认集和试验集,中心组合（二次旋转回归试验设计）田间建模试验得到的20组数据随机划为训练集(17)和确认集(3)数据,试验集为翌年田间确认试验得到的数据,BP神经网络的拓扑结构为3-5-1。（1）BP神经网络对确认集样本的预测值误差分别为1.3550%、0.4291%、0.3538%;（2）BP神经网络的预测值与田间确认试验结果相差为2.04%,回归预测值与田间确认试验结果相差为3.12%;（3）BP神经网络预测比回归预测提高预测精度1.0%以上。将二次多项式逐步回归分析和BP神经网络方法有效的结合使用,既可明确各因子的作用效应亦可得到相对准确的预测结果。     

artificial neural networks based prediction of electromagnetic compatibility problems

It is necessary to predict electromagnetic compatibility (EMC) for electronic equipment and systems. We proposed a fast EMC prediction approach via artificial neural networks (ANN). By choosing relevant electromagnetic interference parameters as the input prediction features, a back propagation (BP) neural network was used to construct the mapping between the input prediction features and the electromagnetic disturbance response of the sensitive system. The EMC fast prediction BP model was trained and tested by sample sets generated using an electromagnetic computational method. We used this method to predict the crosstalk coupling between two wires. The experimental results show the effectiveness of the proposed method.     

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.     

A new pattern recognition method of gas sensor array detection

BP neural network based gas sensor array detection pattern recognition has some disadvantages, such as slow convergence and local minimum problem. A modified immune neural network model which combines BP algorithm and immune algorithm is proposed to enhance global search capability and improve the performance of the neural network model. Orthogonal test is adopted to design the study samples of neural network. This ensures the accuracy of neural network while reducing the number of samples. The simulation results show that the proposed pattern recognition method solves the cross sensitivity of gas sensor effectively, overcomes the disadvantages of traditional BP neural network and improves the learning speed and detection accuracy.     

Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System

While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error.     

BP Neural Networks and Its Application in Lung Cancer Intelligent Diagnosis System

The learning algorithm of networks is discussed. The programming example of 3 layer BP networks is given with Visual C++6.0 program langue. Based on this model, a lung cancer intelligent diagnosis system is successfully implemented. Furthermore, the paper introduces network's structure design, preferencesand the source of stylebookdatum in factual applications. The ameliorative arithmetic is applied to the study of networks and BP dynamic evolving process is designed. The experiments indicatecell images are recognized and classified by the trained neural network. The study illustrates the system has feasibility and clinical value in lung cancer diagnosis.     

The Application of Fuzzy Neural Network in the Detection for Moving Object

A fuzzy neural network(FNN) of detection for moving object based on BP algorithm is described in this paper.The correctness of the FNN in signal detection for moving object and fault diagnosis for instrument is proved by experiments.     

神经网络自变量输入模式的视电阻率求解算法

根据瞬变电磁场理论公式中的响应和自变量之间的关系特点,提出用非线性方程模式的BP神经网络求解电阻率。通过构造单输入单输出网络结构,建立以不同时间点上的电流归一化的感应电压值为输入、视电阻率值为输出的神经网络,来拟合瞬变电磁场的二次涡流曲线。利用数值方法计算出的数据验证该训练网络的精确性,比较了不同算法对训练精度和收敛速度产生的影响。以重庆大学某处的防空洞探测实验为例验证了该算法的有效性,该算法避开具体的复杂电磁场计算或数值反问题计算,从而实现电阻率快速计算,为快速成像准备必要条件。     

Dynamic Prediction Analysis of Surrounding Rocks Stability for Non-symmetric Double-Arch Highway Tunnel

During the construction of non-symmetric double-arch rock highway tunnel, the complicated geological condition may affect the safety of the constructor and the engineering quality. In this paper, two treatment methods are put forward. At first, the site monitoring of surrounding rock displacement must be carried out, then, BP neural network is applied in predicting the displacement of surrounding rock based on the learning sample of measured value, so the stability of surrounding rocks may be analyzed and forecasted. During the analysis of BP neural network, the effects of joint and fracture of surrounding rock on displacement can be comprehensively considered, comparing the predicted values of displacement with those by FEM. The results show that not only the predicted error of BP neural network is relative small, but the predicted values of surrounding rock displacement are close to measured ones. So, the predicted values of BP neural network are reliable and may guide the engineering construction in site.     

基于BP神经网络研究儿茶素在ADS-8树脂固定床中的洗脱过程

摘要：以信阳毛尖茶叶浸提液为原料,研究ADS-8树脂固定床吸附儿茶素后的洗脱过程,建立模型并优化工艺。基于BP神经网络建立洗脱模型,利用模型对因素进行仿真分析。模型误差为0.00108523,测试样本的试验值和模拟值的相关系数r=0.984,最佳工艺条件是温度20℃、流速1.0mL/min、乙醇浓度30%。基于BP神经网络建立的模型具有很强的逼近能力,为儿茶素在ADS-8树脂固定床中洗脱过程的预测、控制提供一定参考。