Small Sample Modelling Based on Deep and Broad Forest Regression
Theory and Industrial Application
Series: Emerging Methodologies and Applications in Modelling, Identification and Control;
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Product details:
- Publisher Academic Press
- Date of Publication 1 November 2025
- ISBN 9780443315640
- Binding Paperback
- No. of pages250 pages
- Size 228x152 mm
- Language English 700
Categories
Long description:
Small Sample Modelling Based on Deep and Broad Forest Regression: Theory and Industrial Application delves into tree-structured methods in the industrial sector, encompassing classical ensemble learning, tree-structured deep forest classification, and broad learning systems with neural networks. It introduces an innovative deep/broad learning algorithm for small-sample industrial modeling tasks. The book is divided into two parts: methodology and practical application in dioxin emission modeling. Methodology sections include Preliminaries, Deep Forest Regression, Broad Forest Regression, and Fuzzy Forest Regression. The application part focuses on modeling dioxin emissions in municipal solid waste incineration. Throughout, various tree-structured strategies are presented, and the authors provide software systems for validating these methods. This book is suitable for advanced undergraduates, graduate engineering students, and practicing engineers looking for self-study resources.
MoreTable of Contents:
PART I Methods
1. Preliminaries
2. Deep Forest Regression for Industrial Modeling
3. Broad Forest Regression for Industrial Modeling
4. Fuzzy Forest Regression for Industrial Modeling
PART II Application to Dioxin Emission Modeling
5. Deep Forest Regression Based on Feature Reduction and Feature Enhancement
6. Simplified Deep Forest Regression with Combined Feature Selection and Residual Error Fitting
7. Online Fuzzy Broad Forest Regression
