In this paper, we discuss drawback of traditional subsection learning algorithm in pattern recognition and exiting support vector machines (including kernel functions), the necessity of using subsection learning algor...In this paper, we discuss drawback of traditional subsection learning algorithm in pattern recognition and exiting support vector machines (including kernel functions), the necessity of using subsection learning algorithm based on support vector machines as well as. In turn, a subsection learning algorithm based on support vector machines, is proposed in this paper.展开更多
采用分段线性电流密度递归卷积(P iecew ise L inear C u rren t D en sity R ecu rsive C onvo lu tion)方法将交替方向隐式时域有限差分方法(AD I-FDTD)推广应用于色散介质—等离子体中,得到了二维情况下等离子体中的迭代差分公式,为...采用分段线性电流密度递归卷积(P iecew ise L inear C u rren t D en sity R ecu rsive C onvo lu tion)方法将交替方向隐式时域有限差分方法(AD I-FDTD)推广应用于色散介质—等离子体中,得到了二维情况下等离子体中的迭代差分公式,为了验证该方法的有效性和可靠性,计算了等离子体涂敷导体圆柱的RC S和非均匀等离子体平板的反射系数,数据仿真结果表明,此算法与传统的FDTD相比,在计算结果吻合的情况下,存储量相当,计算效率更高,时间步长仅仅由计算精度来决定.展开更多
文摘In this paper, we discuss drawback of traditional subsection learning algorithm in pattern recognition and exiting support vector machines (including kernel functions), the necessity of using subsection learning algorithm based on support vector machines as well as. In turn, a subsection learning algorithm based on support vector machines, is proposed in this paper.
文摘采用分段线性电流密度递归卷积(P iecew ise L inear C u rren t D en sity R ecu rsive C onvo lu tion)方法将交替方向隐式时域有限差分方法(AD I-FDTD)推广应用于色散介质—等离子体中,得到了二维情况下等离子体中的迭代差分公式,为了验证该方法的有效性和可靠性,计算了等离子体涂敷导体圆柱的RC S和非均匀等离子体平板的反射系数,数据仿真结果表明,此算法与传统的FDTD相比,在计算结果吻合的情况下,存储量相当,计算效率更高,时间步长仅仅由计算精度来决定.