@article{oai:metro-cit.repo.nii.ac.jp:00000150,
 author = {青木, 立 and Aoki, Tatsu and 川田, 誠一 and Kawata, Seiichi},
 journal = {東京都立産業技術高等専門学校研究紀要, Research reports of Tokyo Metropolitan College of Industrial Technology},
 month = {Mar},
 note = {P(論文), There are power/size and price/cost constraints in realizing embedded mechatronic systems. In order to meetthese specifications, fixed-point microprocessors with a short word-length are suitable for control and real-time identification.From calculation time and accuracy, simple and reliable identification algorithm for fixed-point arithmetic is required.Thus, we proposed previously the simple method that estimates directly physical plant parameters by unifying MRAC anddelta form. However, the proposed method may suffer from parameter estimation errors due to measurement noise. In thispaper signal processing for identification is proposed. The principle is based on noise reduction methodology. High-passand low-pass filter are inserted for plant input and output, respectively so that the input-output relation of the plant doesnot change. Since high-frequency noise or measurement noise is reduced by low-pass filter, S/N ratio of plant output canbe increased. As an illustration, ARX models on first- and second- order systems by using recursive least squares areconsidered. Simulation results show that parameter estimation errors can be greatly reduced by the proposed method.},
 pages = {72--76},
 title = {ノイズリダクション手法を利用したロバストパラメータ推定-逐次最小二乗法に関する検証ー},
 volume = {7},
 year = {2013},
 yomi = {アオキ, タツ and カワタ, セイイチ}
}