A support vector machine-based method for LPV-ARX identification with noisy scheduling parameters.
Farshid AbbasiJavad MohammadpourRoland TóthNader MeskinPublished in: ECC (2014)
Keyphrases
- similarity measure
- high precision
- synthetic data
- high accuracy
- computational cost
- fine tuning
- experimental evaluation
- cost function
- significant improvement
- prior knowledge
- parameter estimation
- computational complexity
- parameter settings
- autoregressive
- dynamic programming
- support vector machine svm
- optimization algorithm
- detection method
- preprocessing
- missing data
- resource allocation
- parameter space
- learning algorithm