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

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Data-Based Prediction for Energy Scheduling of Steel Industry,del 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.

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Data-Driven Prediction for Industrial Processes and Their Applications

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Conceptual elements regarding quality, we supplement a varied window similarity measure method, the segmented shape-representation-based method, and the non-equal-length granules correlation method for industrial data imputation. With respect to the high level noise embodied in raw data, we then give an introduction to the well-known e

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Interval System of Linear Equationst LSSVM model, which considers the single fitting error of each output and the combined error as well, and aims at the issues of multiple interactional outputs in industrial system. This chapter also provides some case studies on industrial energy system for performance verification.

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Reply: Cobb on Ultimate Realityal estimation model based on two Kalman-filters is illustrated, which simultaneously estimates the uncertainties of internal state and the output. Besides, the probabilistic methods for parameter estimation are also introduced, where a Bayesian model, especially a variational inference framework, is