The basic principle of equal base circle bevel gear (EBCBG) is illustrated simply Thetooth surface equation of EBCBG manufactured by end milling cutter with involute profile is de-rived. The tooth form error is analy...The basic principle of equal base circle bevel gear (EBCBG) is illustrated simply Thetooth surface equation of EBCBG manufactured by end milling cutter with involute profile is de-rived. The tooth form error is analyzed on the basis of spherical involute展开更多
Blind equalization based on adaptive forgetting factor, recursive least squares (RLS) with constant modulus algorithm (CMA), is investigated. The cost function of CMA is simplified to meet the second norm form to ...Blind equalization based on adaptive forgetting factor, recursive least squares (RLS) with constant modulus algorithm (CMA), is investigated. The cost function of CMA is simplified to meet the second norm form to ensure the stability of RLS-CMA, and thus an improved RLS-CMA (RLS-SCMA) is established. To further improve its performance, a new adaptive forgetting factor RLS-SCMA (ARLS-SCMA) is proposed. In ARLS-SCMA, the forgetting factor varies with the output error of the blind equalizer during the iterative process, which leads to a faster convergence rate and a smaller steady-state error. The simulation results prove the effectiveness under the condition of the underwater acoustic channel.展开更多
文摘The basic principle of equal base circle bevel gear (EBCBG) is illustrated simply Thetooth surface equation of EBCBG manufactured by end milling cutter with involute profile is de-rived. The tooth form error is analyzed on the basis of spherical involute
基金financially supported in part by the National Natural Science Foundation of China(Grant No.61201418)Fundamental Research Funds for the Central Universities(Grant No.DC12010218)Scientific and Technological Research Project for Education Department of Liaoning Province(Grant No.2010046)
文摘Blind equalization based on adaptive forgetting factor, recursive least squares (RLS) with constant modulus algorithm (CMA), is investigated. The cost function of CMA is simplified to meet the second norm form to ensure the stability of RLS-CMA, and thus an improved RLS-CMA (RLS-SCMA) is established. To further improve its performance, a new adaptive forgetting factor RLS-SCMA (ARLS-SCMA) is proposed. In ARLS-SCMA, the forgetting factor varies with the output error of the blind equalizer during the iterative process, which leads to a faster convergence rate and a smaller steady-state error. The simulation results prove the effectiveness under the condition of the underwater acoustic channel.