標(biāo)題: Titlebook: Data-Driven Prediction for Industrial Processes and Their Applications; Jun Zhao,Wei Wang,Chunyang Sheng Book 2018 Springer International [打印本頁(yè)] 作者: 使固定 時(shí)間: 2025-3-21 18:16
書(shū)目名稱(chēng)Data-Driven Prediction for Industrial Processes and Their Applications影響因子(影響力)
書(shū)目名稱(chēng)Data-Driven Prediction for Industrial Processes and Their Applications影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Data-Driven Prediction for Industrial Processes and Their Applications網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Data-Driven Prediction for Industrial Processes and Their Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Data-Driven Prediction for Industrial Processes and Their Applications被引頻次
書(shū)目名稱(chēng)Data-Driven Prediction for Industrial Processes and Their Applications被引頻次學(xué)科排名
書(shū)目名稱(chēng)Data-Driven Prediction for Industrial Processes and Their Applications年度引用
書(shū)目名稱(chēng)Data-Driven Prediction for Industrial Processes and Their Applications年度引用學(xué)科排名
書(shū)目名稱(chēng)Data-Driven Prediction for Industrial Processes and Their Applications讀者反饋
書(shū)目名稱(chēng)Data-Driven Prediction for Industrial Processes and Their Applications讀者反饋學(xué)科排名
作者: Abominate 時(shí)間: 2025-3-21 23:45
Data Preprocessing Techniques,employed 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作者: Interdict 時(shí)間: 2025-3-22 03:39
Industrial Time Series Prediction,dden 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作者: STALE 時(shí)間: 2025-3-22 07:30
Factor-Based Industrial Process Prediction, of approaches construct a forecasting model by treating the process variables (not the output or target variables) called “factors” as the model inputs, rather than the auto-regression mode used in time series version. To select the factors from lots of candidates, this chapter firstly introduces s作者: 玷污 時(shí)間: 2025-3-22 10:41 作者: 拖債 時(shí)間: 2025-3-22 13:46 作者: 拖債 時(shí)間: 2025-3-22 18:47
Parameter Estimation and Optimization,ed parameter optimization and estimation methods, such as the gradient-based methods (e.g., gradient descend, Newton method, and conjugate gradient method) and the intelligent optimization ones (e.g., genetic algorithm, differential evolution algorithm, and particle swarm optimization). In particula作者: Comprise 時(shí)間: 2025-3-22 23:47
Parallel Computing Considerations,ce a production process usually requires real-time responses. The commonly used method to accelerate the training process is to develop a parallel computing framework. In literature, two kinds of popular methods speeding up the training involves the one with a computer equipped with graphics process作者: 混合,攙雜 時(shí)間: 2025-3-23 03:45 作者: 禁令 時(shí)間: 2025-3-23 06:44
2510-1528 tion, multi-type prediction (time series and factor-based), This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in 作者: 星星 時(shí)間: 2025-3-23 10:44
Colin J. Theaker,Graham R. Brookestrial demands, this book introduces some commonly used prediction techniques, including the time series-based methods, the factor-based methods, the prediction intervals (PIs) construction methods, and the granular-based long-term prediction methods.作者: Orthodontics 時(shí)間: 2025-3-23 13:59 作者: arousal 時(shí)間: 2025-3-23 18:05
Introduction,trial demands, this book introduces some commonly used prediction techniques, including the time series-based methods, the factor-based methods, the prediction intervals (PIs) construction methods, and the granular-based long-term prediction methods.作者: ironic 時(shí)間: 2025-3-23 22:12
Data Preprocessing Techniques,, 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作者: 上流社會(huì) 時(shí)間: 2025-3-24 04:21
Industrial Time Series Prediction,ine (SVM) model, are also presented in this chapter. Specifically, an improved GP-based ESN model is proposed for time series prediction, in which the output weights in ESN modeled by using GP avoids the ill-conditioned phenomenon associated with the generic ESN version. A number of case studies rel作者: 白楊 時(shí)間: 2025-3-24 07:08 作者: reception 時(shí)間: 2025-3-24 11:40 作者: MOAN 時(shí)間: 2025-3-24 16:13 作者: 非實(shí)體 時(shí)間: 2025-3-24 21:05 作者: aspersion 時(shí)間: 2025-3-25 01:50 作者: overweight 時(shí)間: 2025-3-25 04:22
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.作者: 嘲弄 時(shí)間: 2025-3-25 07:40
Data-Driven Prediction for Industrial Processes and Their Applications作者: chance 時(shí)間: 2025-3-25 12:17 作者: 撫慰 時(shí)間: 2025-3-25 16:48 作者: 全部 時(shí)間: 2025-3-25 23:11
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作者: 確定 時(shí)間: 2025-3-26 00:10 作者: 敬禮 時(shí)間: 2025-3-26 07:12
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.作者: 的染料 時(shí)間: 2025-3-26 11:53 作者: 攝取 時(shí)間: 2025-3-26 15:30 作者: 巨頭 時(shí)間: 2025-3-26 19:39
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作者: 構(gòu)想 時(shí)間: 2025-3-27 00:16 作者: 多產(chǎn)魚(yú) 時(shí)間: 2025-3-27 03:41
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.作者: 報(bào)復(fù) 時(shí)間: 2025-3-27 06:06
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), 作者: 捏造 時(shí)間: 2025-3-27 09:34 作者: 舊式步槍 時(shí)間: 2025-3-27 14:53 作者: 放牧 時(shí)間: 2025-3-27 18:34
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作者: opportune 時(shí)間: 2025-3-27 23:17
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作者: 上釉彩 時(shí)間: 2025-3-28 03:34 作者: RAFF 時(shí)間: 2025-3-28 08:36
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.作者: landmark 時(shí)間: 2025-3-28 11:45
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作者: fastness 時(shí)間: 2025-3-28 14:45
Reply: Cobb on Ultimate Realityed parameter optimization and estimation methods, such as the gradient-based methods (e.g., gradient descend, Newton method, and conjugate gradient method) and the intelligent optimization ones (e.g., genetic algorithm, differential evolution algorithm, and particle swarm optimization). In particula作者: 一個(gè)攪動(dòng)不安 時(shí)間: 2025-3-28 22:12
https://doi.org/10.1007/978-1-349-20327-7ce a production process usually requires real-time responses. The commonly used method to accelerate the training process is to develop a parallel computing framework. In literature, two kinds of popular methods speeding up the training involves the one with a computer equipped with graphics process作者: DEAF 時(shí)間: 2025-3-29 02:49
https://doi.org/10.1007/978-1-349-20327-7ted to the optimal scheduling for energy system in steel industry based on the prediction outcomes. As for the by-product gas scheduling problem, a two-stage scheduling method is introduced here. On the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natu作者: PHAG 時(shí)間: 2025-3-29 04:22
Data-Driven Prediction for Industrial Processes and Their Applications978-3-319-94051-9Series ISSN 2510-1528 Series E-ISSN 2510-1536 作者: 種子 時(shí)間: 2025-3-29 09:39
https://doi.org/10.1007/978-3-319-94051-9industrial time series prediction; prediction intervals for industrial data; long term prediction for 作者: Psa617 時(shí)間: 2025-3-29 12:57
978-3-030-06785-4Springer International Publishing AG, part of Springer Nature 2018作者: Nomogram 時(shí)間: 2025-3-29 15:51 作者: RENIN 時(shí)間: 2025-3-29 22:11
