A comparative study of ls-svm’s applied to the silver box identification problem
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Summary
It is possible to compute an approximation for the nonlinear mapping to be used in the primal space using Nystrom techniques, and obtain root mean squared error values of the order of 10 -4 using iterative prediction on a pre-defined test set.
- Type
- article
- Published
- 2004-09-01
- Cited by
- 20
- References
- 18
- OpenAlex
- https://openalex.org/W24105942
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:116206200
Keywords
Mathematics, Support vector machine, Mean squared error, Context (archaeology), Least-squares function approximation
References
- Least Squares Support Vector Machines
- Least squares support vector machines and primal space estimation
- Least Squares Support Vector Machine Classifiers
- Weighted least squares support vector machines: robustness and sparse approximation
- Fast approximate identification of nonlinear systems
- Nonlinear black-box modeling in system identification: a unified overview
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Selection of Variables for Fitting Equations to Data
- Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem
- Using the Nyström Method to Speed Up Kernel Machines
- Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
- Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks
- Networks for approximation and learning
- An Equivalence Between Sparse Approximation and Support Vector Machines
- The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum
- Statistical Learning Theory
- System Identification: Theory for the User
- An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
- The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
Cited by
- Data for benchmarking in nonlinear system identification
- Three free data sets for development and benchmarking in nonlinear system identification
- Implementation of speed controller for rotary hydraulic motor based on LS-SVM
- Identification of MIMO Hammerstein models using least squares support vector machines
- Partially linear support vector machines applied to the prediction of mine slope movements
- Identification of Piecewise Affine LFR Models of Interconnected Systems
- State-of-the-Art and Evolution in Public Data Sets and Competitions for System Identification, Time Series Prediction and Pattern Recognition
- Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model
- Fixed-Size LS-SVM Applied to the Wiener-Hammerstein Benchmark
- Identification of the Silverbox Benchmark Using Nonlinear State-Space Models
- Fixed-size Least Squares Support Vector Machines: A Large Scale Application in Electrical Load Forecasting
- SVD truncation schemes for fixed-size kernel models
- A regularized estimation framework for online sparse LSSVR models
- Reducing Propositional Theories in Equilibrium Logic to Logic Programs
- An outlier-robust kernel RLS algorithm for nonlinear system identification
- Nonlinear System Identification Using Temporal Convolutional Networks: A Silverbox Study
- Simulation of variational Gaussian process NARX models with GPGPU.
- Physical-stochastic continuous-time identification of a forced Duffing oscillator.
- MODELOS NEUROEVOLUCIONÁRIOS COM ECHO STATE NETWORKS APLICADOS À IDENTIFICAÇÃO DE SISTEMAS
- Partially Parametric SVM
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