利用LTE现网数据预测5G网络主要指标及性能的方法

利用LTE现网数据及网络经验,利用各种工具,提出从各个方面对5G网络主要覆盖指标及性能进行预测的方法。

GRNN-PSO视角下的网络安全势态预测研究

网络安全态势预测是属于网络安全态势最为关键构成部分之一,在对网络安全预防制订决策面发挥着非常重要作用。针对网络安全态势预测存在误差等方面问题,本文提出一种GRNN-PSO(改进广义回归神经网络)预测方法,其目的就是使得网络安全态势预估的精准度得到改善。运用时间窗口移动的方法,将各个离散时间监测点的网络安全态势值形成部分线性相关的多元回归数据序列,把它可以看作为样本集录进改进广义回归神经网络中再进行训练,从而形成相关预测模型。在改进广义回归神经网络对其进行训练之中,运用粒子群算法动态地搜索广义回归神经网络最优培训参数,使得选择广义回归神经网络训练参数等方面难题得到解决,最后通过对传统预测方法与改进过的广义回归神经网络预测方法进行相关实验,实验结果体现了前者比后者的方法具有更佳的性能。

基于时间序列分析的航站楼安检旅客流量预测

对单位时间内通过安检的旅客流量进行预测是机场航站楼实时调控的重要依据,由此提出一种实时安检旅客流量预测方法,采用Wolf方法分析出安检旅客流量时间序列具有混沌特性;采用适用于混沌时间序列预测的遗传算法优化BP神经网络预测方法(GABP)预测安检旅客流量;分别设定时间尺度为2 min、5 min和10 min等,分析不同时间尺度对安检旅客流量预测精度的影响。基于北京首都国际机场T3航站楼实际安检旅客流量数据的实验结果表明,采用GABP神经网络对以2 min为时间尺度的安检旅客流量预测能取得较好的预测精准度。

定量定性相结合的企业综合预警方法研究

本文在理论与实践的基础上提出了定量定性相结合的企业综合预警方法。首先应用人工神经网络对企业进行定量预警,并且简要分析了误警概率和虚警概率,然后利用模糊综合评价法对企业环境进行定性预警,最后将两者结合起来达到综合预警的目的。

A new grey forecasting model based on BP neural network and Markov chain

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.

Prediction of Gas Holdup in Bubble Columns Using Artificial Neural Network

A new correlation for the prediction of gas hold up in bubble columns was proposed based on an extensive experimental database set up from the literature published over last 30 years. The updated estimation method relying on artificial neural network, dimensional analysis and phenomenological approaches was used and the model prediction agreed with the experimental data with average relative error less than 10%.

Application and comparison of RNN, RBFNN and MNLR approaches on prediction of flotation column performance

Evaluation of grade and recovery plays an important role in process control and plant profitability in mineral processing operations, especially flotation. The accurate measurement or estimation of these two parameters, based on the secondary variables, is a critical issue. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative. In this paper, two types of artificial neural networks(ANNs),namely radial basis function neural network(RBFNN) and layer recurrent neural network(RNN), and also a multivariate nonlinear regression(MNLR) model were employed to predict metallurgical performance of the flotation column. The training capacity and the accuracy of these three above mentioned types of models were compared. In order to acquire data for the simulation, a case study was conducted at Sarcheshmeh copper complex pilot plant. Based on the root mean squared error and correlation coefficient values, at training and testing stages, the RNN forecasted the metallurgical performance of the flotation column better than RBF and MNLR models. The RNN could predict Cu grade and recovery with correlation coefficients of 0.92 and 0.9, respectively in testing process.

Prediction for asphalt pavement water film thickness based on artificial neural network

In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural network(ANN)a d two-dimensional shallow water equations based on hydrodynamic theory.Multi-factors included the rainfall intensity,pavement width,cross slope,longitudinal slope a d pavement roughness coefficient.The two-dimensional hydrodynamic method was validated by a natural rainfall event.Based on the design scheme o f Shen-Sha expressway engineering project,the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness.Furthermore,the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed.The accuracy o f the ANN model was verified by the18sets o f data with a precision o f0.991.The simulation results indicate that the water film thickness increases from the median strip to the edge o f the pavement.The water film thickness variation is obviously influenced by rainfall intensity.Under the condition that the pavement width is20m and t e rainfall intensity is3m m/h,t e water film thickness is below10mm in the fast lane and20mm in t e lateral lane.Athough there is fluctuation due to the amount oftraining data,compared with the calculation on the basis o f the existing criterion and theory,t e ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film.

An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using Neur-On-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment,and a new modified Broyden, Fletcher, Goldfarb, and Shanno （BFGS） quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation （BP） neural networks is developed and embedded into NeurOn-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quicklv track the time-varving and nonlinear behavior of the bioreactor.

A hybrid model for short-term rainstorm forecasting based on a back-propagation neural network and synoptic diagnosis

Rainstorms are one of the most important types of natural disaster in China.In order to enhance the ability to forecast rainstorms in the short term,this paper explores how to combine a back-propagation neural network(BPNN)with synoptic diagnosis for predicting rainstorms,and analyzes the hit rates of rainstorms for the above two methods using the county of Tianquan as a case study.Results showed that the traditional synoptic diagnosis method still has an important referential meaning for most rainstorm types through synoptic typing and statistics of physical quantities based on historical cases,and the threat score(TS)of rainstorms was more than 0.75.However,the accuracy for two rainstorm types influenced by low-level easterly inverted troughs was less than 40%.The BPNN method efficiently forecasted these two rainstorm types;the TS and equitable threat score(ETS)of rainstorms were 0.80 and 0.79,respectively.The TS and ETS of the hybrid model that combined the BPNN and synoptic diagnosis methods exceeded the forecast score of multi-numerical simulations over the Sichuan Basin without exception.This kind of hybrid model enhanced the forecasting accuracy of rainstorms.The findings of this study provide certain reference value for the future development of refined forecast models with local features.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Times Series Prediction to Basis of a Neural Network Conceived by a Real Genetic Algorithm

Neural network and genetic algorithms are complementary technologies in the design of adaptive intelligent system. Neural network learns from scratch by adjusting the interconnections betweens layers. Genetic algorithms are a popular computing framework that uses principals from natural population genetics to evolve solutions to problems. Various forecasting methods have been developed on the basis of neural network, but accuracy has been matter of concern in these forecasts. In neural network methods forecasted values depend to the choose of neural predictor structure, the number of the input, the lag. To remedy to these problem, in this paper, the authors are investing the applicability of an automatic design of a neural predictor realized by real Genetic Algorithms to predict the future value of a time series. The prediction method is tested by using meteorology time series that are daily and weekly mean temperatures in Melbourne, Australia, 1980-1990.

Analysis on Application of Wavelet Neural Network in Wind Electricity Power Prediction

Wind electricity power has fluctuation, and accurate and reasonable wind electricity power prediction is very important for solving wind electricity network and combination. This paper takes an analysis of a lot of actual data of a certain wind electricity field. Through wavelet neural network and time series method rolling, it can predict the overall power of wind electricity field. The result shows that for the original data of sampling time length and large sampling frequency, the model constructed by this paper has very good prediction effect. Because of the fan installation position, wind electricity fan flow effect and other random factor influence, wind electricity field overall power and single unit power distribution have difference. Through comparing with the time series parameters, it puts forward that single wind electricity unit power has smooth effect for overall power of wind electricity field. Finally, it summarizes the prediction effect and puts forward some reasonable suzestions for wind electricity network troblems.