Data-Driven Prediction for Industrial Processes and Their Applications
-15%
portes grátis
Data-Driven Prediction for Industrial Processes and Their Applications
Sheng, Chunyang; Zhao, Jun; Wang, Wei
Springer International Publishing AG
08/2018
443
Dura
Inglês
9783319940502
15 a 20 dias
852
Descrição não disponível.
Preface.- Introduction.- Why the prediction is required for industrial process.- Introduction to industrial process prediction.- Category of industrial process prediction.- Common-used techniques for industrial process prediction.- Brief summary.- Data preprocessing techniques.- Anomaly detection of data.- Correction of abnormal data.- Methods of packing missing data.- Data de-noising techniques.- Data fusion methods.- Discussion.- Industrial time series prediction.- Introduction.- Methods of phase space reconstruction.- Prediction modeling.- Benchmark prediction problems.- Cases of industrial applications.- Discussion.- Factor-based industrial process prediction.- Introduction.- Methods of determining factors.- Factor-based single-output model.- Factor-based multi-output model.- Cases of industrial applications.- Discussion.- Industrial Prediction intervals with data uncertainty.- Introduction.- Common-used techniques for prediction intervals.- Prediction intervals with noisy outputs.- Prediction intervals with noisy inputs and outputs.- Time series prediction intervals with missing input.- Industrial cases of prediction intervals.- Discussion.- Granular computing-based long term prediction intervals.- Introduction.- Basic theory of granular computing.- Techniques of granularity partition.- Long-term prediction model.- Granular-based prediction intervals.- Multi-dimension granular-based long term prediction intervals.- Discussion.- Parameters estimation and optimization.- Introduction.- Gradient-based methods.- Evolutionary algorithms.- Nonlinear Kalman-filter estimation.- Probabilistic methods.- Gamma-test based noise estimation.- Industrial applications.- Discussion.- Parallel computing considerations.- Introduction.- CUDA-based parallel acceleration.- Hadoop-based distributed computation.- Other techniques.- Industrial applications to parallel computing.- Discussion.- Prediction-based scheduling of industrial system.- Introduction.- Scheduling of blast furnace gas system.- Scheduling of coke oven gas system.- Scheduling of converter gas system.- Scheduling of oxygen system.- Predictive scheduling for plant-wide energy system.- Discussion.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
industrial time series prediction;prediction intervals for industrial data;long term prediction for industrial time series;nonlinear noisy time series prediction;Time scale-based classification;techniques for industrial process prediction;quality control, reliability, safety and risk
Preface.- Introduction.- Why the prediction is required for industrial process.- Introduction to industrial process prediction.- Category of industrial process prediction.- Common-used techniques for industrial process prediction.- Brief summary.- Data preprocessing techniques.- Anomaly detection of data.- Correction of abnormal data.- Methods of packing missing data.- Data de-noising techniques.- Data fusion methods.- Discussion.- Industrial time series prediction.- Introduction.- Methods of phase space reconstruction.- Prediction modeling.- Benchmark prediction problems.- Cases of industrial applications.- Discussion.- Factor-based industrial process prediction.- Introduction.- Methods of determining factors.- Factor-based single-output model.- Factor-based multi-output model.- Cases of industrial applications.- Discussion.- Industrial Prediction intervals with data uncertainty.- Introduction.- Common-used techniques for prediction intervals.- Prediction intervals with noisy outputs.- Prediction intervals with noisy inputs and outputs.- Time series prediction intervals with missing input.- Industrial cases of prediction intervals.- Discussion.- Granular computing-based long term prediction intervals.- Introduction.- Basic theory of granular computing.- Techniques of granularity partition.- Long-term prediction model.- Granular-based prediction intervals.- Multi-dimension granular-based long term prediction intervals.- Discussion.- Parameters estimation and optimization.- Introduction.- Gradient-based methods.- Evolutionary algorithms.- Nonlinear Kalman-filter estimation.- Probabilistic methods.- Gamma-test based noise estimation.- Industrial applications.- Discussion.- Parallel computing considerations.- Introduction.- CUDA-based parallel acceleration.- Hadoop-based distributed computation.- Other techniques.- Industrial applications to parallel computing.- Discussion.- Prediction-based scheduling of industrial system.- Introduction.- Scheduling of blast furnace gas system.- Scheduling of coke oven gas system.- Scheduling of converter gas system.- Scheduling of oxygen system.- Predictive scheduling for plant-wide energy system.- Discussion.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
