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

• Covers the benefits and an explanation of recent developments in prediction and soft sensing systems
• Unifies existing and emerging concepts concerning advanced prediction models/architectures
• Provides a series of the latest results in, including but not limited to, supervised learning, semi-supervised learning, self-supervised learning, probabilistic learning