WAVELETS IN INTELLIGENT TRANSPORTATION SYSTEMS增强计算智能的子波：附智能运输系统应用
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作者： Hojjat Adeli等著
出 版 社：
出版时间： 2005-11-1字数：版次： 1页数： 224印刷时间： 2005/11/01开本： 16开印次： 1纸张： 胶版纸I S B N ： 9780470867426包装： 精装内容简介
This book shows how wavelets can be used to enhance computational intelligence for chaotic and complex pattern recognition problems. By integrating wavelets with other soft computing techniques such as neurocomputing and fuzzy logic, complicated and noisy pattern recognition problems can be solved effectively. The book focuses on applications in intelligent transportation systems (ITS) where a number of very complicated pattern recognition problems have eluded researchers over the past few decades.
Advancing the frontiers of computational intelligence, this book:
Describes ingenious computational models based on novel problem solving and computing techniques such as Case-Based Reasoning, Neurocomputing, and Wavelets, and presents examples to illustrate their importance and use.
Presents a multi-paradigm intelligent systems approach to the freeway traffic incident detection and construction work zone management problems.
Advocates application and integration of wavelets, neural networks and fuzzy logic for modeling the complex traffic flow behaviors leading to effective and efficient control and management solutions.
Presents efficient, reliable, and robust algorithms for automatic detection of incidents on freeways.
Wavelets in Intelligent Transportation Systems is an invaluable resource for computational intelligence researchers and transportation engineers involved in the application of advanced computational techniques for ITS.
目录
Preface
Acknowledgment
About the Authors
1.Introduction
2.Introduction to Wavelet Analysis
2.1 Introduction
2.2 Basic Concept of Wavelets and Wavelet Analysis
2.2.1 What is a Wavelet?
2.2.2 Wavelet Analysis
2.2.3 Types of Wavelets and Wavelet Transforms
2.3 Mathematical Foundations
2.3.1 Sets and Spaces
2.3.2 Sequence and Function Spaces
2.3.3 Independent and Basis Sets
2.3.4 Metric, Normed and Inner Product Spaces
2.3.5 The L2(R) and L2(Z) Spaces
2.3.6 Orthogonality
2.4 The Discrete Wavelet Transform (DWT)
2.5 Multi-resolution Analysis
2.6 Wavelet Bases
2.6.1 Constructing Wavelet Bases
2.6.2 Example Wavelet Systems
2.7 Computing the DWT
2.7.1 Pyramid Algorithm
2.7.2 Practical Considerations
3.Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis
3.1 Introduction
3.2 Incident Detection Algorithms
3.3 Discrete Wavelet Transform (DWT) of Traffic Signals
3.4 Linear Discriminant Analysis (LDA)
3.5 Data Acquisition
3.6 Results
4.Adaptive Conjugate Neural Network-Wavelet Model for Traffic Incident Detection
4.1 Introduction
4.2 Improving Traffic Incident Detection
4.3 Adaptive Conjugate Gradient Neural Network Model
4.4 Incident Detection Results Using Various Approaches
4.4.1 LDA
4.4.2 DWT and LDA
4.4.3 ACGNN
4.4.4 DWT, LDA, and ACGNN
4.5 Effect of Data Filtering Using DWT
4.6 Relative Contribution of DWT and LDA for Feature Extraction
4.7 Effects of Freeway Geometry on Incident Detection
4.7.1 Effect of Curvature
4.7.2 Effect of Number of Lanes
4.8 Conclusion
5.Enhancing Fuzzy Neural Network Algorithms Using Neural Networks
5.1 Introduction
5.2 Discrete Wavelet Transform
5.3 Architecture
5.4 Training of the Network
5.5 Filtering of the Traffic Data Using DWT
5.6 Incident Detection Results
6.Fuzzy-Wavelet Radial Basis Function Neural Network Model for Freeway Incident Detection
7.Comparison of Fuzzy-Wavelet RBFNN Freeway Incident Detection Model with California Algorithm
8.Incident Detection Algorithm Using Wavelet Energy Representation of Traffic Patterns
9.Parametric Evaluation of the Wavelet Energy Freeway Incident Detection Algorithm
10.Case-Based Reasoning Model for Work Zone Traffic Management
11.Mesoscopic-Wavelet Freeway Work Zone Flow and Congestion Model
References
Index
