Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on s...Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on some weapon tracking servo system, a three layer BPNN was used to off line identify the backlash characteristics, then a nonlinear compensator was designed according to the identification results. Results The simulation results show that the method can effectively get rid of the sustained oscillation(limit cycle) of the system caused by the backlash characteristics, and can improve the system accuracy. Conclusion The method is effective on sloving the problems produced by the backlash characteristics in servo systems, and it can be easily accomplished in engineering.展开更多
Analysis and comparison of Jiaozhou Bay data collected from May 1991 to February 1994 revealed the spatiotemporal variations of the ambient Si(OH) 4∶NO 3 (Si∶N) concentration ratios and the seasonal variations of (S...Analysis and comparison of Jiaozhou Bay data collected from May 1991 to February 1994 revealed the spatiotemporal variations of the ambient Si(OH) 4∶NO 3 (Si∶N) concentration ratios and the seasonal variations of (Si∶N) ratios in Jiaozhou Bay and showed that the Si∶N ratios were < 1 throughout Jiaozhou Bay in spring, autumn, and winter. These results provide further evidence that silicate limits the growth of phytoplankton (i.e. diatoms) in spring, autumn and winter. Moreover, comparison of the spatiotemporal variations of the Si∶N ratio and primary production in Jiaozhou Bay suggested their close relationship. The spatiotemporal pattern of dissolved silicate matched well that of primary production in Jiaozhou Bay. Along with the environmental change of Jiaozhou Bay in the last thirty years, the N and P concentrations tended to rise, whereas Si concentration showed cyclic seasonal variations. With the variation of nutrient Si limiting the primary production in mind, the authors found that the range of values of primary production is divided into three parts: the basic value of Si limited primary production, the extent of Si limited primary production and the critical value of Si limited primary production, which can be calculated for Jiaozhou Bay by Equations (1), (2) and (3), showing that the time of the critical value of Si limitation of phytoplankton growth in Jiaozhou Bay is around November 3 to November 13 in autumn; and that the time of the critical value of Si satisfaction of phytoplankton growth in Jiaozhou Bay is around May 22 to June 7 in spring. Moreover, the calculated critical value of Si satisfactory for phytoplankton growth is 2.15-0.76 μmol/L and the critical value of Si limitation of phytoplankton growth is 1.42-0.36 μmol/L; so that the time period of Si limitation of phytoplankton growth is around November 13 to May 22 in the next year; the time period of Si satisfactory for phytoplankton growth is around June 7 to November 3. This result also explains why critical values of nutrient silicon affect phytoplankton growth in spring and autumn are different in different waters of Jiaozhou Bay and also indicates how the silicate concentration affects the phytoplankton assemblage structure. The dilution of silicate concentration by seawater exchange affects the growth of phytoplankton so that the primary production of phytoplankton declines outside Jiaozhou Bay earlier than inside Jiaozhou Bay by one and half months. This study showed that Jiaozhou Bay phytoplankton badly need silicon and respond very sensitively and rapidly to the variation of silicon.展开更多
文摘Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on some weapon tracking servo system, a three layer BPNN was used to off line identify the backlash characteristics, then a nonlinear compensator was designed according to the identification results. Results The simulation results show that the method can effectively get rid of the sustained oscillation(limit cycle) of the system caused by the backlash characteristics, and can improve the system accuracy. Conclusion The method is effective on sloving the problems produced by the backlash characteristics in servo systems, and it can be easily accomplished in engineering.
文摘Analysis and comparison of Jiaozhou Bay data collected from May 1991 to February 1994 revealed the spatiotemporal variations of the ambient Si(OH) 4∶NO 3 (Si∶N) concentration ratios and the seasonal variations of (Si∶N) ratios in Jiaozhou Bay and showed that the Si∶N ratios were < 1 throughout Jiaozhou Bay in spring, autumn, and winter. These results provide further evidence that silicate limits the growth of phytoplankton (i.e. diatoms) in spring, autumn and winter. Moreover, comparison of the spatiotemporal variations of the Si∶N ratio and primary production in Jiaozhou Bay suggested their close relationship. The spatiotemporal pattern of dissolved silicate matched well that of primary production in Jiaozhou Bay. Along with the environmental change of Jiaozhou Bay in the last thirty years, the N and P concentrations tended to rise, whereas Si concentration showed cyclic seasonal variations. With the variation of nutrient Si limiting the primary production in mind, the authors found that the range of values of primary production is divided into three parts: the basic value of Si limited primary production, the extent of Si limited primary production and the critical value of Si limited primary production, which can be calculated for Jiaozhou Bay by Equations (1), (2) and (3), showing that the time of the critical value of Si limitation of phytoplankton growth in Jiaozhou Bay is around November 3 to November 13 in autumn; and that the time of the critical value of Si satisfaction of phytoplankton growth in Jiaozhou Bay is around May 22 to June 7 in spring. Moreover, the calculated critical value of Si satisfactory for phytoplankton growth is 2.15-0.76 μmol/L and the critical value of Si limitation of phytoplankton growth is 1.42-0.36 μmol/L; so that the time period of Si limitation of phytoplankton growth is around November 13 to May 22 in the next year; the time period of Si satisfactory for phytoplankton growth is around June 7 to November 3. This result also explains why critical values of nutrient silicon affect phytoplankton growth in spring and autumn are different in different waters of Jiaozhou Bay and also indicates how the silicate concentration affects the phytoplankton assemblage structure. The dilution of silicate concentration by seawater exchange affects the growth of phytoplankton so that the primary production of phytoplankton declines outside Jiaozhou Bay earlier than inside Jiaozhou Bay by one and half months. This study showed that Jiaozhou Bay phytoplankton badly need silicon and respond very sensitively and rapidly to the variation of silicon.
