Year: 2014
Type: article
Abstract: Focus on this issue of disturbance and fault value is inevitable in data collection about analog circuit. A novel strategy is developed for analog circuit online performance evaluation based on fuzzy learning and double weighted support vector machine (DWMK‐FSVM). First, the double weighted support vector regression machine is employed to be the indirect evaluation means, relied on the college analog electronic technology experiment to evaluate analog circuit. Second, the superiority of fuzzy learning also is addressed to realize active suppression to the fault values and disturbance parameters. Moreover, the multikernel RBF is employed by support vector regression machine to realize more flexibility online such as the bandwidths tuning. Numerical results, supported by the college analog circuit experiments, adopted OTL performance eight indexes, which were obtained via precision instrument evaluation in two years to construct training set and are then to be evaluated online based on DWMK‐FSVM. Simulation results presented not only highlight precision of the evaluation strategy derived here but also illustrate its great robustness.
DOI: 10.1155/2014/728201
Authors: Aihua Zhang, Yongchao Wang, Chen Chen, Hamid Reza Karimi
Institutions: Bohai University, University of Agder
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $579
Awards: G4577609609, G523548552, G6722524816
OpenAlex ID: W2074433799