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1.
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。 相似文献
2.
A new pattern recognition method of gas sensor array detection 总被引：1，自引：0，他引：1
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. 相似文献
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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%. 相似文献
4.
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. 相似文献
5.
ZHANG Xin-yan 《保鲜与加工》2004,(5):54-56
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. 相似文献
6.
Stratification and Bearing Capacity Prediction Method Based on BP Neural Network for Foundation in Huaibei Plain 总被引：1，自引：0，他引：1
According to the features of stratification and obvious inhomogeneity in geological soil in Huaibei plain, BP neural network prediction method for stratification and bearing capacity calculation of multiple cross-bedded foundation was proposed. By comparing the results of drill sampling, static cone penetration tests and screw plate tests, plate loading tests, penetration resistance ps value was found as an evaluation index for stratification and bearing capacity prediction of cross-bedded foundation. Moreover, gradient descent algorithm and conjugate gradient algorithm BP neural network models were obtained, and the calculation results of the two algorithms were comparatively analyzed. The results show that penetration resistance value can be taken as an evaluation index for stratification and bearing capacity prediction of cross-bedded foundation in Huaibei plain. Gradient descent algorithm and conjugate gradient algorithm BP neural network models have good results for soil identification and bearing capacity determination, which can meet the accuracy requirements of actual engineering. However, the computational efficiency of gradient descent algorithm is significantly lower than that of conjugate gradient algorithm. 相似文献
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基于布谷鸟搜索神经网络的微波加热温度预测模型 总被引：1，自引：0，他引：1
微波加热是一种与被加热物直接相互作用的选择性加热方式,具有清洁、节能、减排等特点。针对工业物料作为微波加热负载时,其温度非线性变化的特点,以微波工业加热过程中的多维、海量参数为研究对象,基于泛函接神经网络模型提取样本数据的深度特征,提出了一种基于布谷鸟搜索算法,优化BP神经网络的网络参数,建立了以"数据驱动"为手段微波加热工业物料温度模型。仿真实验结果证明了所提出模型的准确性、实时性。 相似文献
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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。 相似文献
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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. 相似文献
10.
The load of air condition system is influenced by many factors, and they are variable and nonlinear, The relation between them is dynamic,It is impossible to forecaste the load of air condition syestem accurately by traditional method. But Recurrent Neural Network is able to reflect the dynamic lively and directly. Elman is one of the typical RNN. Based on the analysis as above, prediction model of air-condition system based on Elman neural network is established, and some prediction is done. The prediction accuracy of Elman neural network and BP neural network is compared, and the experiments show that the Elman neural network is efficiency and accuracy , so Elman neural network is a new and reliable method for predicting the load of air-condition system. 相似文献
11.
The existing problems of the traditional weight integrating forecast methods and the application in climate prediction are analyzed. A new method based on data mining is presented, which uses BP artificial neural network to build the integrating forecast classifier to integrate the forecast results of sub-methods. According to the features of different forecast objects, this method can change weight dynamically, which overcomes the shortage of the traditional weight integrating forecasts that cannot change weight after been decided and overcomes the shortage that cannot get the optimal results. By predicting the precipitation and average temperature of Chengkou County in January, and spring drought index of Chongqing from 2001 to 2007, the experiment results show that the reliability and accuracy of the proposed model are better than those of the sub-methods and other integrating forecast methods, which proves the effectiveness of this method. 相似文献
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【目的】研究以玉米地上干生物量为研究对象,探讨基于无人机高光谱数据利用人工神经网络法反演生物量的可行性。【方法】在吉林省蔡家镇开展玉米氮肥梯度试验,并进行无人机高光谱数据和地上干生物量获取,共获数据30组。随机选22组数据用于建模,剩下8组用于模型的外部验证。分别基于光谱指数法和BP神经网络算法构建反演模型,比较分析各种方法反演玉米生物量的优劣。【结果】结果表明：和基于光谱指数构建的生物量反演模型相比,BP神经网络模型取得了更好的反演结果。其建模时决定系数为0.99均方根误差为0.08 t/ha,相对均方根误差为3.39%;外部验证时,决定系数为0.99,均方根误差为0.15 t/ha,相对均方根误差为8.56%。【结论】BP神经网络模型可有效提高无人机高光谱遥感反演玉米地上生物量的精度。 相似文献
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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。 相似文献
14.
To solve the instability problem of established sample in the neural network evaluation method for mine ventilation system, a comprehensive evaluation of the ventilation system is carried out based on rough sets and BP neural networks. Taking the ventilation system of a mine as an example, the classification quality of raw data samples are tested by using rough set data analysis system. Then, based on artificial neural network theory, a rough sets-neural network evaluation model of a mine ventilation system is established and a new rough sets-neural network evaluation method of mine ventilation system is formed. The results show that, after the model validation of data and application, its theoretical evaluation results are in line with the actual situation, and the network total error is less than 0.004. It shows that the comprehensive evaluation method based on rough sets-neural networks has a good effect in evaluating mine ventilation system in practical application. 相似文献
15.
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。 相似文献
16.
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。 相似文献
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The very fast Transient over-voltage generated due to the switchgear in GIS switching off or on is very harmful to transformer insulation. The model presented is a winding hybrid lumped parameter model which combines the branch model based on parallel cables in one turn with grouped-turns model of two discs to efficiently simulate the very fast transient in the coil. Under quasi-stationary electromagnetic field, network topology and element size are derived from the geometry and properties of the transformer, whose inductance and capacitance are assumed to be passive and to have linear characteristics, however which have an inherent feature of a medium, which propagates waves. The formula of parameters are obtained. Based on the built winding model, the voltage gradients between discs and turns are found out, according to the simulation results, the reasonable winding insulation design can be made. 相似文献
19.
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. 相似文献
20.
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。 相似文献