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

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Abominate

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

Interdict

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

STALE

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

玷污

拖債

拖債

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

Comprise

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

混合,攙雜

禁令

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