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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dongyan Zhang Jun Cao Liping Sun |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Electromechanics Engineering, Northeast Forestry University, Harbin 150040, China (Dongyan Zhang; Jun Cao; Liping Sun) |
| Abstract | Least Squares Support Vector Machines(LSSVM) regression principle and measure methods of moisture content during wood drying were introduced. Wood moisture content is a key parameter for regulating and controlling wood drying proces. In this paper soft sensor model based on LSSVM was established for the weakness of wood moisture content measurement in drying process, and parameters selection adopted improved exhaust algorithm. The simulation results of Fraxinus mandshurica and Xylosma racemosum showed that the LSSVM soft sensor model had well robustness and generalization ability, and could predict wood moisture content measurement in drying process accurately, which offered an effective approach for measuring the parameters in the complicated and nonlinear process of wood drying. |
| File Size | 164575 |
| File Format | |
| ISBN | 9781424438631 |
| DOI | 10.1109/ICEMI.2009.5274395 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-16 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Soft Sensor Predictive models Wood Moisture Content Electrical resistance measurement Modeling Support vector machines Temperature sensors Moisture control Electromagnetic measurements Moisture measurement Humidity Robustness Least Squares Support Vector Machines(LSSVM) Electromechanical sensors |
| Content Type | Text |
| Resource Type | Article |
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