Combined Prediction for Vehicle Speed with Fixed Route

Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning,but the prediction accuracy still needs to be improved.The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy;problems,such as over fitting,occur in the process of improving prediction accuracy.The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction.By combining the two prediction algorithms,the fusion of prediction performance is achieved,the limit of the single prediction performance is crossed,and the goal of improving vehicle speed prediction performance is achieved.In this paper,an extraction method suitable for fixed route vehicle speed is designed.The application of Markov and back propagation(BP)neural network in predictions is introduced.Three new combined prediction methods,all named Markov and BP Neural Network(MBNN)combined prediction algorithm,are proposed,which make full use of the advantages of Markov and BP neural network algorithms.Finally,the comparison among the prediction methods has been carried out.The results show that the three MBNN models have improved by about 19%,28%,and 29%compared with the Markov prediction model,which has better performance in the single prediction models.Overall,the MBNN combined prediction models can improve the prediction accuracy by 25.3%on average,which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

OPTIMAL COMBINING PREDICTION FOR BLENDING EFFICIENCY

By means of analysing the mechanism of blending materials,a general blending efficiency model was proposed.Applying this general model to an example 9 a suitable formula of blending efficiency which is more accurate than those in papers[2-3]was obtained.Finally,a high-precision optimal combining prediction formula for calculating blending efficiency was proposed.

Prediction and Optimization Performance Models for Poor Information Sample Prediction Problems

The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.

Grey series time-delay predicting model in state estimation for power distribution networks

A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.

Combined prediction of rockburst based on multiple factors and stacking ensemble algorithm

Rockburst is a kind of common geological disaster in deep tunnel engineering.It has the characteristics of causing great harm and occurring at random locations and times.These characteristics seriously affect tunnel construction and threaten the physical and mental health and safety of workers.Therefore,it is of great significance to study the tendency of rockburst in the early stage of tunnel survey,design and construction.At present,there is no unified method and selected parameters for rockburst prediction.In view of the large difference of different rockburst criteria and the imbalance of rockburst database categories,this paper presents a two-step rockburst prediction method based on multiple factors and the stacking ensemble algorithm.Considering the influence of rock physical and mechanical parameters,tunnel face conditions and excavation disturbance,multiple rockburst criteria are predicted by integrating multiple machine learning algorithms.A combined prediction model of rockburst criteria is established,and the results of each rockburst criterion index are weighted and combined,with the weight updated using the field rockburst record.The dynamic weight is combined with the cloud model to comprehensively evaluate the regional rockburst risk.Field results from applying the model in the Grand Canyon tunnel show that the rockburst prediction method proposed in this paper has better applicability and higher accuracy than the single rockburst criterion.

Development cost prediction of general aviation aircraft using combined estimation technique

Obtaining accurate development cost estimation results of general aviation aircraft is crucial for companies to adopt the best strategy in the development process.To address this problem,this paper proposes a combination of three commonly used single prediction methods.The optimal weight values of the three single prediction methods are determined by utilizing the shortest ideal point method.Ten cost datasets collected from literature are utilized for fitting and testing the combined prediction method,and the weight coefficients of the three individual prediction methods are calculated as 0.6859,0.0035 and 0.3106,respectively.The results of this study indicate that the developed method has better fitting and estimation accuracy than that of the three individual methods,with average fitting and predicting error values of 2.60%and 6.43%,respectively.Additionally,the cost data of military and civil aircraft development from literature are collected for verification.The results further confirm that the proposed method is not only superior to the single prediction methods in terms of high precision but has wider applications.More importantly,this research can provide important reference for general aviation aircraft companies in term of product cost planning and corporate sales strategies.