Journal ArticleDOI
Significant wave height estimation using SVR algorithms and shadowing information from simulated and real measured X-band radar images of the sea surface
Sancho Salcedo-Sanz,J.C. Nieto Borge,L. Carro-Calvo,Lucas Cuadra,Katrin Hessner,Enrique Alexandre +5 more
TLDR
This paper shows that SVR can be successfully trained from simulation-based data, and shows the performance of the SVR in simulation data and how SVR outperforms alternative algorithms such as neural networks.About:
This article is published in Ocean Engineering.The article was published on 2015-06-01. It has received 66 citations till now. The article focuses on the topics: Sea state & Wave height.read more
Citations
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Journal ArticleDOI
Regional ocean wave height prediction using sequential learning neural networks
TL;DR: A study to predict the daily wave heights in different geographical regions using sequential learning algorithms, namely the Minimal Resource Allocation Network (MRAN) and the Growing and Pruning Radial Basis Function (GAP-RBF) network.
Journal ArticleDOI
Ocean Wind and Wave Measurements Using X-Band Marine Radar: A Comprehensive Review
TL;DR: The goal of this paper is to provide a comprehensive review of the state of the art algorithms for ocean wind and wave information extraction from X-band marine radar data.
Journal ArticleDOI
Computational intelligence in wave energy: Comprehensive review and case study
TL;DR: This paper reviews those used in wave energy applications, both in the resource estimation and in the design and control of wave energy converters, and illustrates the potential of hybridizing a Coral Reefs Optimization algorithm with an Extreme Learning Machine to tackle the problem of significant wave height reconstruction.
Journal ArticleDOI
Bayesian optimization of a hybrid system for robust ocean wave features prediction
TL;DR: It is shown that BO can be used to obtain the optimal parameters of a prediction system for problems related to ocean wave features prediction, and a hybrid Grouping Genetic Algorithm for attribute selection combined with an Extreme Learning Machine approach for prediction is proposed.
Journal ArticleDOI
Ocean wave height prediction using ensemble of Extreme Learning Machine
TL;DR: From this study, it is inferred that the Ens-ELM out performs ELM, Online Sequential ELM (OS- ELM), and Support Vector Regression (SVR) in the daily wave height prediction.
References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Journal ArticleDOI
Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
Book
Introduction to Radar Systems
TL;DR: This chapter discusses Radar Equation, MTI and Pulse Doppler Radar, and Information from Radar Signals, as well as Radar Antenna, Radar Transmitters and Radar Receiver.
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