智能算法用于汽油馏程蒸发温度的计算

探讨了人工神经网络法和径向基神经网络法,用于GB/T6536《石油产品蒸馏测定法》测定汽油馏程试验时,从馏出温度计算蒸发温度的可能性。试验表明,线性神经网络法的泛化预测能力,优于径向基神经网络法。用所选的训练集数据建立数学模型后,测试集数据的仿真结果表明,仿真值与原测定值的绝对偏差,没有超过2℃,说明线性神经网络法用于汽油蒸发温度的计算是可行的,该方法是一种快速、准确、简便值得借鉴的方法。

A prediction comparison between univariate and multivariate chaotic time series

The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic time series including local mean prediction, local linear prediction and BP neural networks prediction are considered. The simulation results obtained by the Lorenz system show that no matter what nonlinear prediction method is used, the prediction error of multivariate chaotic time series is much smaller than the prediction error of univariate time series, even if half of the data of univariate time series are used in multivariate time series. The results also verify that methods to determine the time delays and the embedding dimensions are correct from the view of minimizing the prediction error.

Backlash Nonlinear Compensation of Servo Systems Using Backpropagation Neural Networks

Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on some weapon tracking servo system, a three layer BPNN was used to off line identify the backlash characteristics, then a nonlinear compensator was designed according to the identification results. Results The simulation results show that the method can effectively get rid of the sustained oscillation(limit cycle) of the system caused by the backlash characteristics, and can improve the system accuracy. Conclusion The method is effective on sloving the problems produced by the backlash characteristics in servo systems, and it can be easily accomplished in engineering.

Inversion of 3D density interface with PSO-BP method

BP( Back Propagation) neural network and PSO( Particle Swarm Optimization) are two main heuristic optimization methods,and are usually used as nonlinear inversion methods in geophysics. The authors applied BP neural network and BP neural network optimized with PSO into the inversion of 3D density interface respectively,and a comparison was drawn to demonstrate the inversion results. To start with,a synthetic density interface model was created and we used the proceeding inversion methods to test their effectiveness. And then two methods were applied into the inversion of the depth of Moho interface. According to the results,it is clear to find that the application effect of PSO-BP is better than that of BP network. The BP network structures used in both synthetic and field data are consistent in order to obtain preferable inversion results. The applications in synthetic and field tests demonstrate that PSO-BP is a fast and effective method in the inversion of 3D density interface and the optimization effect is evident compared with BP neural network merely,and thus,this method has practical value.

Application of 2-D Position Sensitive Detector in Spatial Straightness Measurement of Guide Rails

A laser collimating system based on 2-D position sensitive detector (PSD) is presented in this paper. The working principle of PSD is depicted in detail. A calibration device was developed to check the nonlinearity errors of PSD and a multilayer feedforward neural network based on error back-propagation algorithm was used to compensate errors. With the aid of computer-based data acquisition system, an automatic dynamic measuring process was realized. A series of experiments, including comparison tests with laser interferometer, were done to evaluate the performance of the measuring system. The experimental results show that the spatial straightness errors of guide rails can be measured with high accuracy. The maximum differences between the device and laser interferometer are 0.027 mm in Y direction, and 0.053 mm in X direction in the measuring distance of 6 m.

Study on the non-linear forecast method for water inrush from coal seam floor based on wavelet neural network

Directing at the non-linear dynamic characteristics of water inrush from coal seam floor and by the analysis of the shortages of current forecast methods for water inrush from coal seam floor, a new forecast method was raised based on wavelet neural network （WNN） that was a model combining wavelet function with artificial neural network. Firstly basic principle of WNN was described, then a forecast model for water inrush from coal seam floor based on WNN was established and analyzed, finally an example of forecasting the quantity of water inrush from coal floor was illustrated to verify the feasibility and superiority of this method. Conclusions show that the forecast result based on WNN is more precise and that using WNN model to forecast the quantity of water inrush from coal seam floor is feasible and practical.

Research on fuzzy neural network algorithms for nonlinear network traffic predicting

This paper addresses the use of fuzzy neural networks （FNN） for predicting the nonlinear network traffic. Through training the fuzzy neural networks with momentum back-propagation algorithm （MOBP） and choosing the appropriate activation function of output node, the traffic series can be well predicted by these structures. From the effective forecasting results obtained, it can be concluded that fuzzy neural networks can be well applicable for the traffic series prediction. In addition,the performance of the FNN was particularly discussed and analyzed in terms of prediction ability compared with solely neural networks. The effectiveness of the oroBosecl FNN is demonstrated through the simulation.

Nonlinear modeling of PEMFC based on neural networks identification

The proton exchange membrane generation technology is highly efficient and clean, and is considered as the most hopeful “green” power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model. This paper first simply analyzes the necessity of the PEMFC generation technology, then introduces the generating principle from four aspects: electrode, single cell, stack, system; and then uses the approach and self-study ability of artificial neural network to build the model of nonlinear system, and adapts the Leven- berg-Marquardt BP (LMBP) to build the electric characteristic model of PEMFC. The model uses experimental data as training specimens, on the condition the system is provided enough hydrogen. Considering the flow velocity of air (or oxygen) and the cell operational temperature as inputs, the cell voltage and current density as the outputs and establishing the electric characteristic model of PEMFC according to the different cell temperatures. The voltage-current output curves of model has some guidance effect for improving the cell performance, and provide basic data for optimizing cell performance that have practical significance.

Power Big Data Fusion Prediction

This paper is a research on the characteristics of power big data. According to the characteristics of ＂large volume＂, ＂species diversity＂, ＂sparse value density＂, ＂fast speed＂ of the power big data, a prediction model of multi-source information fusion for large data is established, the fusion prediction of various parameters of the same object is realized. A combined algorithm of Map Reduce and neural network is used in this paper. Using clustering and nonlinear mapping ability of neural network, it can effectively solve the problem of nonlinear objective function approximation, and neural network is applied to the prediction of fusion. In this paper, neural network model using multi layer feed forward network--BP neural network. Simultaneously, to achieve large-scale data sets in parallel computing, the parallelism and real-time property of the algorithm should be considered, further combined with Reduce Map model, to realize the parallel processing of the algorithm, making it more suitable for the study of the fusion of large data. And finally, through simulation, it verifies the feasibility of the proposed model and algorithm.

Finite Convergence of On-line BP Neural Networks with Linearly Separable Training Patterns

In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.

混凝土桥梁裂缝宽度监测与预测研究

桥梁裂缝宽度是混凝土桥梁结构在运营阶段的重点观测参数。针对目前监测系统中频繁出现的预警误报情况,本文提出了一种基于中值滤波和非线性自回归神经网络法(NARNN)预测桥梁结构裂缝的方法。选取某混凝土梁桥的4条裂缝数据进行预测,并对输出值与目标值比较及均方误差(MSE)进行精度控制和检验,发现该方法可有效地预测桥梁结构裂缝宽度,可减少监测系统中预警误报的情形。

FORECASTING TIME SERIES WITH GENETIC PROGRAMMING BASED ON LEAST SQUARE METHOD

Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： Genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.