Small Sample Modelling Based on Deep and Broad Forest Regression : Theory and Industrial Application (Emerging Methodologies and Applications in Modelling, Identification and Control)

Yu, Wen/ Tang, Jian/ Qiao, Junfei

Academic Press Inc（2025/11発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 352 p.
• 言語 ENG
• 商品コード 9780443315640
• DDC分類 658.5

Full 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.

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