Analysis methods of SrTiO<Subscript>3</Subscript> ceramic’s electricity performance

The effect of oxidizing-heat-treatment conditions on the electricity performance of doped SrTiO3 ceramic is analyzed by using the theory of grey neural network. Based on the number of main parameters, the model of GNNM （1,1）, GNNM （1,2）, GNNM （1,3） is used to analyze and construct the corresponding model of GNNM （2,1） gray neural network. It can reach the required precision by calculating.     

Hybrid grey model to forecast monitoring series with seasonality

The grey forecasting model has been successfully applied to many fields. However, the precision of GM（1,1） model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM （1,1） model, the forecasting series of GM（1,1） was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i. e. , seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM（1,1） model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM（1,1） model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM（1,1） model. The mean absolute error and mean square error of seasonal index-GM（1,1） are 30.69 % and 54.53 % smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.     

A novel recurrent neural network forecasting model for power intelligence center

In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network （UMRNN） for power intelligence center （PIC） was created through three steps. First, by combining with the general project uncertain element transmission theory （GPUET）, the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors： generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error （ERMS） demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network （BPNN）, and 5% higher than that of autoregressive integrated moving average （ARIMA）. Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.     

Forecasting increasing rate of power consumption based on immune genetic algorithm combined with neural network

Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.     

Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm

Nonlinear resistivity inversion requires efficient artificial neural network （ANN） model for better inversion results. An evolutionary BP neural network （BPNN） approach based on differential evolution （DE） algorithm was presented, which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion. In the proposed method, Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter Fcrit was used to enhance the ability of converging to global optimum. An implementation of proposed DE-BPNN was given, the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets. Two abnormity models were used to verify the feasibility and effectiveness of the proposed method, the results show that the proposed DE-BP algorithm has better performance than BP, conventional DE-BP and other chaotic DE-BP methods in stability and accuracy, and higher imaging quality than least square inversion.     

Effective prediction of DEA model by neural network

In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training algorithm.The SPDS training algorithm overcomes the drawbacks of slow convergent speed and partially minimum result for BP algorithm.Its training speed is much faster and its forecasting precision is much better than those of BP algorithm.By numeric examples,it is showed that adopting the neural network model in the forecasting of effective points by DEA model is valid.     

Study of a New Improved PSO-BP Neural Network Algorithm

In order to overcome shortcomings of traditional BP neural network, such as low study efficiency, slow convergence speed, easily trapped into local optimal solution, we proposed an improved BP neural network model based on adaptive particle swarm optimization （PSO） algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP （IPSO-BP） model, it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals, and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP, PSO-BP and linear PSO-BP （LPSO-BP） algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed, lower errors, higher diagnostic accuracy and learning ability.     

Application of functional-link neural network in evaluation of sublayer suspension based on FWD test

Several methods for evaluating the sublayer suspension beneath old pavement with falling weight deflec-tormeter(FWD), were summarized and the respective advantages and disadvantages were analyzed. Based on these methods, the evaluation principles were improved and a new type of the neural network, functional-link neural net-work was proposed to evaluate the sublayer suspension with FWD test results. The concept of function link, learn-ing method of functional-link neural network and the establishment process of neural network model were studied in detail. Based on the old pavement over-repairing engineering of Kaiping section, Guangdong Province in G325 Na-tional Highway, the application of functional-link neural network in evaluation of sublayer suspension beneath old pavement based on FWD test data on the spot was investigated. When learning rate is 0.1 and training cycles are 405, the functional-link network error is less than 0.0001, while the optimum chosen 4-8-1 BP needs over 10000 training cycles to reach the same accuracy with less precise evaluation results. Therefore, in contrast to common BP neural network, the functional-link neural network adopts single layer structure to learn and calculate, which simpli-fies the network, accelerates the convergence speed and improves the accuracy. Moreover the trained functional-link neural network can be adopted to directly evaluate the sublayer suspension based on FWD test data on the site. En-gineering practice indicates that the functional-link neural model gains very excellent results and effectively guides the pavement over-repairing construction.     

Electricity price forecasting using generalized regression neural network based on principal components analysis

A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.     

Soft sensor for ratio of soda to aluminate based on PCA-RBF multiple network

Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than3%, and the model has good generalization ability.     

基于粒子群算法和BP神经网络改进的灰色电力负荷预测研究

灰色预测模型被广泛运用于电力负荷预测中,取得了较好的效果,但是灰色预测模型在实际应用中的缺点和局限性导致其预测精度有待提高,存在改进的必要。本文对于灰色预测模型的改进,分别从优化初值和改进模型等方面进行,从而提高普通灰色GM（1,1）模型的预测精度。对初值的处理可以削弱异常值的影响,强化趋势,从而避免由于初值选择不当而造成预测误差。本文中对模型的改进主要通过建立等维新息预测模型、灰色粒子群组合预测模型和灰色BP神经网络组合预测模型来实现。通过这些对灰色预测模型的修正和改进,进一步提高了灰色预测模型的适用性．最大限唐妯提高了灰乍．GM（1,1）模型的预测精唐．     

灰色马尔柯夫预测模型

概要地介绍了灰色系统理论的研究对象，引入了ＧＭ（１，１）灰色预测模型，并将灰色预测与马尔柯夫预测方法相结合建立了一种对ＧＭ（１，１）模型进行了预测物新方法，克服两种预测法的不足，提高了预测精度。最后用一例子说明灰色马尔柯夫预测方法的应用。     

基于灰色BP神经网络组合模型的郑州市商品住宅价格预测

针对郑州市商品住宅价格问题进行了研究,在传统GM(1,1)模型的基础上引入BP神经网络模型,建立了灰色BP神经网络组合模型,采用传统GM(1,1)模型与灰色BP神经网络组合模型预测郑州市商品住宅价格.结果表明,灰色BP神经网络组合模型比传统GM(1,1)模型预测精度高,具有更好的应用价值.     

基于有机灰色神经网络模型的空气污染指数预测

针对灰色预测对波动较强的序列只能预测大致变化趋势的缺陷，结合灰理论中的GM(1，1)、无偏GM(1，1)、非等时距GM(1，1)、pGM(1，1)和BP神经网络的特点，提出有机灰色神经网络预测模型，将一维序列通过三个灰色模型得到三组值作为神经网络的输入，原始序列作为神经网络的输出，训练得到最佳神经网络结构．以哈尔滨市近三年内空气污染指数为例，结合其变化规律，建立哈尔滨市月平均空气污染指数的有机灰色神经网络预测模型，结果表明，该模型拟合误差小，预测精度高．     

基于改进灰色神经网络的房地产价格走势分析

针对房地产价格走势状况,通过对灰色预测模型GM（1,1）和BP神经网络的研究,将两大模型进行组合改良,形成新的组合灰色神经网络预测模型,以南京市中房指数为例,以Matlab为预测工具,进行2013年12个月份的价格指数预测,研究结果证明新的组合预测模型精度较高,可为房地产价格指数的预测和研究提供参考依据。     

Analysis Methods of SrTiO3 Ceramic''''s Electricity Performance

The effect of oxidizing-heat-treatment conditions on the electricity performance of doped SrTiO3 ceramic is analyzed by using the theory of grey neural network. Based on the number of main parameters, the model of GNNM (1,1), GNNM (1,2), GNNM (1,3) is used to analyze and construct the corresponding model of GNNM (2,1) gray neural network. It can reach the required precision by calculating.     

基于GANO算法的电力系统中长期负荷预测研究

进行负荷预测时,由于中长期负荷历史数据较少而制约因素较多,因此预测难度较大。在分析了灰色预测和神经网络预测优缺点的基础上,提出了多因素灰色神经网络组合预测模型(GANO)。该模型首先采用灰色GM(1,n)模型处理多因素的影响,进而利用BP神经网络训练电力历史负荷数据,最后利用统计方差的倒数建立较为理想的优化组合预测模型。该优化模型结合了各模型优点且综合考虑了电力负荷的多种制约因素。经算例验证,优于单一历史负荷预测模型,有效地提高了中长期负荷预测精度。     

基于熵权法的PHC管桩承载力组合预测

为克服单项预测方法产生的误差,利用灰色模型GM（1,N）、多元线性回归、BP神经网络等3种单项预测方法建立组合预测模型,并采用熵值法确定加权系数。通过对PHC管桩承载力进行比较预测,结果显示GM（1,N）法平均绝对百分比误差（MAPE）值为5.4%,多元线性回归法的MAPE为3.0%,BP神经网络法的MAPE为2.8%,组合预测法的MAPE为2.3%。因此组合预测法精度较高,实用性更强。