A novel method of process dead-time identification: support vector machine approach
2004
Performance and robustness of model-based control system are sensitive to the modeling error, especially to the dead-time identification error. Support vector machine (SVM) employs structure risk minimization principle to control model complexity and the upper bound of generalization risk. If the seeking dead-time contained in training data equals dead time of actual plant, the trained SVM has the lowest complexity. The identification procedure is described as follows. Firstly, specify a dead-time seeking range based on the prior process knowledge. Secondly, construct training data sets from input-output data according to different dead times in seeking range and train SVMs respectively. Finally, the estimated dead-time can be obtained through comparing the numbers of support vectors of all trained SVMs. A lot of discrete simulations for the first order plus dead-time system have been done to illuminate the effectiveness of proposed method.
Keywords:
- Error detection and correction
- Dead time
- Errors-in-variables models
- Robustness (computer science)
- Robust control
- Support vector machine
- Structural risk minimization
- Control engineering
- Control theory
- Machine learning
- Discrete event simulation
- Computer science
- Artificial intelligence
- Upper and lower bounds
- Minification
- Open-loop controller
- Correction
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