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000856237 019__ $$a1049820420$$a1049934663$$a1050565412$$a1055596468
000856237 020__ $$a9783319940519$$q(electronic book)
000856237 020__ $$a3319940511$$q(electronic book)
000856237 020__ $$z9783319940502
000856237 020__ $$z3319940503
000856237 0247_ $$a10.1007/978-3-319-94051-9$$2doi
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000856237 050_4 $$aTS183$$b.Z53 2018eb
000856237 08204 $$a670.285$$223
000856237 1001_ $$aZhao, Jun,$$eauthor.
000856237 24510 $$aData-driven prediction for industrial processes and their applications /$$cJun Zhao, Wei Wang, Chunyang Sheng.
000856237 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2018]
000856237 300__ $$a1 online resource :$$billustrations.
000856237 336__ $$atext$$btxt$$2rdacontent
000856237 337__ $$acomputer$$bc$$2rdamedia
000856237 338__ $$aonline resource$$bcr$$2rdacarrier
000856237 347__ $$atext file$$bPDF$$2rda
000856237 4901_ $$aInformation fusion and data science
000856237 504__ $$aIncludes bibliographical references and index.
000856237 5050_ $$aIntro; Preface; Audience and Goal of This Book; Acknowledgements; Contents; Chapter 1: Introduction; 1.1 Why Prediction Is Required for Industrial Process; 1.2 Category of Data-Based Industrial Process Prediction; 1.2.1 Data Feature-Based Prediction; 1.2.2 Time Scale-Based Prediction; 1.2.3 Prediction Reliability-Based Prediction; 1.3 Commonly Used Techniques for Industrial Prediction; 1.3.1 Time Series Prediction Methods; 1.3.2 Factor-Based Prediction Methods; 1.3.3 Methods for PIs Construction; 1.3.4 Long-Term Prediction Intervals Methods; 1.4 Summary; References.
000856237 5058_ $$aChapter 2: Data Preprocessing Techniques2.1 Introduction; 2.2 Anomaly Data Detection; 2.2.1 K-Nearest-Neighbor; 2.2.2 Fuzzy C Means; 2.2.3 Adaptive Fuzzy C Means; 2.2.4 Trend Anomaly Detection Based on AFCM and DTW; 2.2.5 Deviants Detection Based on KNN-AFCM; 2.2.6 Case Study; 2.3 Data Imputation; 2.3.1 Data-Missing Mechanism; 2.3.2 Regression Filling Method; 2.3.3 Expectation Maximum; 2.3.4 Varied Window Similarity Measure; 2.3.5 Segmented Shape-Representation Based Method; Key-Sliding-Window for Sequence Segmentation; Representation of Sequence Segmentation.
000856237 5058_ $$aProcedure of Data Imputation Based on Segmented Shape-Representation2.3.6 Non-equal-Length Granules Correlation; Calculation for NGCC; NGCC-Based Correlation Analysis; Correlation-Based Data Imputation; 2.3.7 Case Study; 2.4 Data De-noising Techniques; 2.4.1 Empirical Mode Decomposition; 2.4.2 Case Study; 2.5 Discussion; References; Chapter 3: Industrial Time Series Prediction; 3.1 Introduction; 3.2 Phase Space Reconstruction; 3.2.1 Determination of Embedding Dimensionality; False Nearest-Neighbor Method (FNN); Cao Method; 3.2.2 Determination of Delay Time; Autocorrelation Function Method.
000856237 5058_ $$aMutual Information Method3.2.3 Simultaneous Determination of Embedding Dimensionality and Delay Time; 3.3 Linear Models for Regression; 3.3.1 Basic Linear Regression; 3.3.2 Probabilistic Linear Regression; 3.4 Gaussian Process-Based Prediction; 3.4.1 Kernel-Based Regression; 3.4.2 Gaussian Process for Prediction; 3.4.3 Gaussian Process-Based ESN; 3.4.4 Case Study; 3.5 Artificial Neural Networks-Based Prediction; 3.5.1 RNNs for Regression; 3.5.2 ESN for Regression; 3.5.3 SVD-Based ESN for Industrial Prediction; 3.5.4 ESNs with Leaky Integrator Neurons; 3.5.5 Dual Estimation-Based ESN.
000856237 5058_ $$a3.5.6 Case StudyExtended Kalman-Filter-Based Elman Network; SVD-Based ESN for Industrial Prediction; ESN with Leaky Integrator Neurons; Dual Estimation-Based ESN; 3.6 Support Vector Machine-Based Prediction; 3.6.1 Basic Concept of SVM; 3.6.2 SVMs for Regression; 3.6.3 Least Square Support Vector Machine; 3.6.4 Sample Selection-Based Reduced SVM; 3.6.5 Bayesian Treatment for LSSVM Regression; Probabilistic Interpretation of LSSVM Regressor (Level 1): Predictive Mean and Error Bars; Calculation of Maximum Posterior; Moderated Output of LSSVM Regressor; Inference of Hyper-Parameters (Level 2).
000856237 506__ $$aAccess limited to authorized users.
000856237 520__ $$aThis 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 the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.
000856237 588__ $$aDescription based on online record; title from digital title page (viewed on December 11, 2018).
000856237 650_0 $$aManufacturing processes$$xMathematical models.
000856237 650_0 $$aIndustrial engineering.
000856237 7001_ $$aWang, Wei,$$eauthor.
000856237 7001_ $$aSheng, Chunyang,$$eauthor.
000856237 77608 $$iPrint version:$$aZhao, Jun.$$tData-driven prediction for industrial processes and their applications.$$dCham, Switzerland : Springer, [2018]$$z3319940503$$z9783319940502$$w(OCoLC)1037044847
000856237 830_0 $$aInformation fusion and data science.
000856237 852__ $$bebk
000856237 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-94051-9$$zOnline Access$$91397441.1
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000856237 980__ $$aEBOOK
000856237 980__ $$aBIB
000856237 982__ $$aEbook
000856237 983__ $$aOnline
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