- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
Learning-Based Predictions and Soft Sensing for Process Industries covers prediction and soft sensing in industrial processes subject to specific challenges with AI-empowered learning algorithms. With the aid of a data-driven modeling strategy, the book explores the problems of industrial prediction and soft sensing, and formulates a series of learning-based theory, methodology and applications. The book introduces the basics of the prediction and soft sensing backgrounds, including the different categories of prediction theory. Secondly, the book looks at the foundations of machine learning methodologies, which covers supervised learning prediction, semi-supervised and self-supervised prediction. Finally, the book examines some novel learning-based models/architectures
Contents
Section 1: Theory
1. Introduction of Prediction
2. Theoretical Foundations of Paste-Filling System
3. Foundation of Aluminium Electrolysis System
Section 2: Methodology
4. Machine Learning Basics for Prediction & Soft Sensing
Section 3: Application
5. A Novel Supervised Soft Sensor Framework Based on Convolutional Laplacian Extreme Learning Machine: CNN-LapsELM
6. A Novel Semi-Supervised Soft Sensor Framework Based on Stacked Auto-Encoder Wavelet Extreme Learning Machine: SAE-WELM
7. A Novel Soft Sensor Based on Laplacian Hessian Semi-Supervised Hierarchical Extreme Learning Machine: LHSS-HELM
8. A Self-Supervised Prediction Framework Based on Deep Long Short-Time Memory for Aluminum Electrolysis: SSDLSTM
9. A Self-Supervised Prediction Framework Based on Convolutional Deep Long Short-Time Memory for Aluminum Temperature Application: CNN-SSDLSTM
10. A Novel Probabilistic Prediction Framework Based on Bayesian Machine Learning: BLSTM
11. Direct Data-Driven Quantile Regressor Forecaster for Underflow Concentration Soft Sensing: DDQRF
12. A Novel Key-Quality Prediction Framework for Industrial Deep Cone Thickener: DualLSTM
13. A Deeply-Efficient Long Short-Time Memory Framework for Underflow Concentration Prediction: DE-LSTM
14. An Ensemble Prediction Method for Probabilistic Forecasting of Aluminium Electrolysis Process
