提出了一种基于大步距标定和局部牛顿(Newton)插值的焊接参数自调节算法,不仅可以实现参数调节的一元化,还将数字化焊机的数据库由静态数据库模式转变为具有自学习和自调节功能的动态数据库模式,为数字弧焊电源向智能化方向发展提供了...提出了一种基于大步距标定和局部牛顿(Newton)插值的焊接参数自调节算法,不仅可以实现参数调节的一元化,还将数字化焊机的数据库由静态数据库模式转变为具有自学习和自调节功能的动态数据库模式,为数字弧焊电源向智能化方向发展提供了一种有效的演进策略.结果表明,这种算法可以实现数字化焊机参数在较大范围内的连续自调节;用生成的脉冲型熔化极气体保护焊(pulsed gas metal arc welding,P-GMAW)参数进行试焊,焊接过程稳定,焊缝成形良好;采用具有不同优先级的焊接参数存储模式可以实现焊接参数的最优化.展开更多
There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fi...There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.展开更多
文摘提出了一种基于大步距标定和局部牛顿(Newton)插值的焊接参数自调节算法,不仅可以实现参数调节的一元化,还将数字化焊机的数据库由静态数据库模式转变为具有自学习和自调节功能的动态数据库模式,为数字弧焊电源向智能化方向发展提供了一种有效的演进策略.结果表明,这种算法可以实现数字化焊机参数在较大范围内的连续自调节;用生成的脉冲型熔化极气体保护焊(pulsed gas metal arc welding,P-GMAW)参数进行试焊,焊接过程稳定,焊缝成形良好;采用具有不同优先级的焊接参数存储模式可以实现焊接参数的最优化.
基金Project(60574030) supported by the National Natural Science Foundation of ChinaKey Project(60634020) supported by the National Natural Science Foundation of China
文摘There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.
