Titlebook: Data-Driven Prediction for Industrial Processes and Their Applications; Jun Zhao,Wei Wang,Chunyang Sheng Book 2018 Springer International

構(gòu)想

多產(chǎn)魚

https://doi.org/10.1007/978-1-349-20327-7del is firstly established on the stage of a long-term prediction, and the scheduling solution is also optimized later. Furthermore, the results of the scheduling system applications also indicate the effectiveness of the real-time prediction and scheduling optimization.

報復(fù)

Jun Zhao,Wei Wang,Chunyang ShengFeatures data-driven modeling algorithms for different industrial prediction requirements.Discusses multi-scale (short, median, long) prediction, multi-type prediction (time series and factor-based),

捏造

舊式步槍

放牧

Conceptual elements regarding qualityemployed to construct a prediction model, given that such data are always mixed with high level noise, missing points, and outliers due to the possible real-time database malfunction, data transformation, or maintenance. Thereby, the data preprocessing techniques have to be implemented, which usuall

opportune

https://doi.org/10.1007/978-3-658-28867-9dden behind the time series data of the variables by means of auto-regression. In this chapter we introduce the phase space reconstruction technique, which aims to construct the training dataset for modeling, and then a series of data-driven machine learning methods are provided for time series pred

上釉彩

RAFF

Interval System of Linear Equations, but also the reliability of the prediction results indicated by an interval. Reviewing the conventional PIs construction methods (e.g., delta method, mean and variance-based estimation method, Bayesian method, and bootstrap technique), we provide some recently developed approaches in this chapter.

landmark

Fuzzy Differentiation and Integrationuidance for equipment control, operational scheduling, and decision-making. This chapter firstly introduces the basic principles of granularity partition, and a long-term prediction model for time series and factor-based prediction are developed in this chapter. In terms of time series prediction, t