Wavelets in Intelligent Transportation Systems
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Wavelets in Intelligent Transportation Systems

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ISBN-13:
9780470867426
Einband:
Buch
Erscheinungsdatum:
01.11.2005
Seiten:
224
Autor:
Hojjat Adeli
Gewicht:
476 g
Format:
231x160x19 mm
Sprache:
Englisch
Beschreibung:

Shows how wavelets can be used to enhance computational intelligence for chaotic and complex pattern recognition problems. This book describes ingenious computational models based on novel problem solving and computing techniques, and presents examples to illustrate their importance and use.
From the contents:

Preface.

Acknowledgment.

About the Authors.

1. Introduction.

2. Introduction to Wavelet Analysis.

2.1 Introduction.

2.2 Basic Concept of Wavelets and Wavelet Analysis.

2.3 Mathematical Foundations.

2.4 The Discrete Wavelet Transform (DWT).

2.5 Multi-resolution Analysis.

2.6 Wavelet Bases.

2.7 Computing the DWT.

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.

4.5 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.

4.8 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.

6.1 Introduction.

6.2 A New Traffic Incident Detection Methodology.

6.3 Selection of Type and Number of Traffic Data.

6.4 Wavelet-Based De-noising.

6.5 Fuzzy Data Clustering.

6.6 Radial Basis Function Neural Network Classifier.

6.7 Fuzzy-Wavelet RBFNN Model for Incident Detection.

6.8 Example.

6.9 Conclusion.

7. Comparison of Fuzzy-Wavelet RBFNN Freeway Incident Detection Model with California Algorithm.

7.1 Introduction.
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.