共查询到20条相似文献，搜索用时 31 毫秒
1.
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. 相似文献
2.
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
3.
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
4.
CHEN Qiu-nan~ 《保鲜与加工》2005,(1):62-66
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. 相似文献
5.
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
6.
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
7.
This paper has presented a multi-objective fuzzy optimal power flow medel.Inthe model , the multiple objectives, such as the minimum generation cost and the minimum powerloss, have been considered simultaneously, A new algorithm based on neural network models is aisopresented,in which the neural networks are employed to express, the membership function of fuzzysets and solve the optimization problems. The validity of model and algorithm is verified with numerical examples. 相似文献
8.
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. 相似文献
9.
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. 相似文献
10.
11.
Yan Ping Yi Runzhong Liu Fei Ma Zujun 《保鲜与加工》1998,(4):7-11
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. 相似文献
12.
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. 相似文献
13.
Pressure is measured at different levels in the loop layer because self adapting predictive decoupling control systems are strongly coupled, disturbed, and non linear and there is a long time delay for gas collector pressure systems in coke ovens. By combing the traditional neural network control and proportional integral differential(PID) controllers based on radial basis function(RBF) neural network identification, the gas collector pressure is ensured to reach the desired technology range. The prediction model of an RBF neural network is used for advanced prediction of the actual output pressure to overcome delays in general gas collection. The simulation results and application indicate that the method can obtain ideal control results. 相似文献
14.
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 总被引：1，自引：0，他引：1
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。 相似文献
15.
Fast algorithm for apparent resistivity calculation based on back propagation neural networks 总被引：1，自引：1，他引：0
To avoid the complex numerical calculation for the electromagnetic field and determine underground abnormality, a neural network based method is proposed. In consideration of turn off transmitter current, the effect of a linear ramp turn off current on transmitter is corrected. The characteristics of transient expression and the traditional calculation algorithm for apparent resistivity are analyzed, and a predigest structure of network is obtained based on the kernel expression. The three layer back propagation(BP) neural network is trained by using sample data in homogeneous half space, and its number in hidden layer was determined. The method proposed is compared with two traditional calculation methods with simulation experiments. The result demonstrates that BP neural network has a high speed of processing data and is useful in explanation of the transient electromagnetic method. 相似文献
16.
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
17.
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。 相似文献
18.
基于随机森林法的棉花叶片叶绿素含量估算 总被引：3，自引：0，他引：3
为了高效和无损地估算棉花叶片的叶绿素含量,本研究测定了棉花光谱反射率及叶绿素含量(soilandplant analyzerdevelopment,SPAD)值,对光谱数据进行包络线去除处理、立方根转换和倒数转换,以SPAD值与反射光谱之间的相关性为基础,通过随机森林法筛选出对棉花叶片SPAD值影响较大的特征波段,构建估算棉花叶片SPAD值的BP神经网络(back propagation artificial neural networks, BP ANN)、偏最小二乘回归(partial least squares regression,PLSR)两个模型。结果表明,在605～690nm范围内的反射率与SPAD值相关性达0.01显著水平,均呈负相关,相关系数最高值为-0.619。与原始光谱相比,经过变换后的棉花反射率与SPAD值相关性结果相差较大,其中去除包络线光谱在550～750 nm波段范围有效提高了相关性,相关性效果优于倒数转换数据和立方根转换数据。随机森林法能够有效评出对SPAD值影响较大的特征波段,进而提高模型估算精度。在两种模型中,基于去除包络线光谱建立的PLSR和BP神经网络模型的决定系数R~2分别为0.92、0.83,说明这两种模型的估算能力较好;两种模型RMSE分别为0.88、1.26, RE分别为1.30%、1.89%,表明PLSR模型的估算精度比BP神经网络模型高。从模型的验证效果来看,PLSR模型在估算棉花SPAD值方面有一定的优势和参考价值。 相似文献
19.
The determination of fuzzy membership function in the fuzzy support vector machine (FSVM) is a difficult problem. To solve the problem of being sensitive to the noises and outliers in support vector machine, by the inspiration of Bayesian decision theory, combining with sample density characteristics, sample points relation between same class and other class is researched, and the tightness on each sample points is described. Based on that, method of posterior probability and sample density weight are given to each sample, and new fuzzy membership function is proposed. The detection of the noises and outliers is avoided by this method. Numerical simulation shows that the improved fuzzy membership function method is effective. 相似文献
20.
温室栽培基质耗水量与环境因子相关性的研究 总被引：1，自引：1，他引：0
为指导温室精确灌溉,对温室内气温、湿度、光照与不同栽培基质水分蒸发量进行了回归分析,根据纯水日蒸发量回归分析了不同栽培基质的水分日蒸发量,并使用BP神经网络对番茄需水量进行模拟。结果表明回归方程法能够较为准确地模拟日光温室水日蒸发量和基质水分日蒸发量,使用温室水蒸发量对未种植作物的园田土基质蒸发的回归模拟能取得较好的结果,而使用BP人工神经网络能够较好地对种植番茄的园田土水分日消耗量进行模拟。 相似文献