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9780817643478

Neural Networks And Sea Time Series

by ; ;
  • ISBN13:

    9780817643478

  • ISBN10:

    0817643478

  • Format: Hardcover
  • Copyright: 2006-01-01
  • Publisher: Birkhauser

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Supplemental Materials

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Summary

This self-contained book is devoted to the application of neural networks to the concrete problem of handling the time series of sea data, i.e., significant wave heights and sea levels. Beginning with an explanation of neural networks fundamentals for beginners, the authors follow with a review of general statements and rigorous facts, and conclude with applications and algorithms for particular data sets having well-defined parameters.Specifically analyzed are the time series of measures observed from a system of sea buoys, as well as the reconstruction of missing values that are largely present in these time series. Neural networks are used to solve this problem, and the reconstructed time series are modeled to estimate the probability of large events using extreme value theory. This ability to model and estimate is important for the construction of sea structures, ports, and marine experiments."Neural Networks and Sea Time Series" is a unique monograph on the topic and may stimulate new research and results in the field. The book will be a useful resource for a diverse audience of graduate students, researchers and practitioners in applied mathematics, data analysis, meteorology, hydraulic, civil and marine engineering.

Table of Contents

Preface ix
Introduction
1(8)
General Remarks
1(2)
Plan of the Book
3(1)
Introduction to Waves
4(3)
Introduction to Tides
7(2)
Basic Notions on Waves and Tides
9(14)
Definitions of SWH
9(2)
Experimental Apparatus
11(1)
The National Sea Wave Measurement Network (RON)
12(3)
Tides
15(2)
Tide Gauges
17(3)
The National Sea Level Measurement Network (RMN)
20(1)
Conclusions
20(3)
The Wave Amplitude Model
23(12)
The WAM
23(1)
The Free Surface Problem
24(2)
The Linearized System
26(1)
Wave Packet
27(2)
The Action Balance Equation
29(6)
Artificial Neural Networks
35(24)
Introduction to Neural Networks
35(5)
Embedding Dimension
40(2)
Three Methods
41(1)
Learning Algorithms
42(5)
Steepest Descent
42(1)
Simulated Annealing
43(4)
Rigorous Results
47(12)
Vapnik-Chervonenkis Theorem
47(2)
Support Vector Machine
49(2)
Statistical Mechanics Approach
51(5)
Extreme-Value Theory
56(3)
Approximation Theory
59(8)
General Position of the Problem
59(1)
Explanation of the Mathematics
60(1)
Approximation Operators
61(6)
Extreme-Value Theory
67(24)
The Case of i.i.d. Variables and the POT Method
67(8)
Introduction
67(2)
The Results
69(6)
Extreme-Value Theory for Stationary Random Processes
75(4)
Introduction
75(1)
Exact Formulation
76(3)
Process of Exceedances
79(5)
Introduction
79(1)
Point Processes and Poisson Processes
80(2)
The i.i.d. Case
82(1)
The Stationary Case
83(1)
Extremal Index
84(7)
Introduction
84(1)
Summary of the Theory
84(3)
Practical Estimates of θ
87(4)
Application of ANN to Sea Time Series
91(20)
The SWH Time Series and Its Correlation Properties
91(4)
The SL Time Series and Its Correlation Properties
95(1)
The Input Vectors for SWH and SL
95(1)
Neural Networks and Sea Time Series
96(12)
RMN and NN
96(6)
RON and NN
102(6)
Real-Time Application of Artificial Neural Networks
108(3)
Application of Approximation Theory and ARIMA Models
111(22)
Approximation Operator in One-Dimensional Case
111(1)
Approximation for the SWH
112(3)
ARIMA Models
115(18)
Preliminary Analysis on SHW Measures
116(2)
Sampling
118(2)
Identification of the Model
120(3)
Estimate of the Model
123(2)
Prediction: Finding Missing SHW Values
125(8)
Extreme-Event Analysis
133(22)
Preliminary Analysis
133(3)
Model Fitting
136(1)
Results of the Extreme-Event Analysis
137(4)
Results: Alghero's Site
138(3)
Results for Other Sites
141(1)
Neural Networks and Sea Storm Reconstructions
141(14)
Data Analysis
144(3)
The NN System
147(2)
Time-Series Reconstruction Results and Extreme-Event Statistic
149(3)
Extreme-Event Reconstruction
152(3)
Generalization to Other Phenomena
155(12)
The Adaptive NN Algorithm and Its Learning Phase
156(1)
The ANN Performance with One or More Time-Series Inputs
157(5)
NN and Precipitation Forecast
162(2)
Postprocessing of Temperatures
164(3)
Conclusions
167(4)
Summary
167(1)
Open Problems
168(3)
References 171(4)
Index 175

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