1) local strongly accretive mapping
局部强增殖映象
2) local strongly accretive
局部强增殖
1.
A related result deals with the problem that the Ishikawa itera-tion process converges strongly to a solution of the equat ion Tx=f when T is Lipschitzian and local strongly accretive in X.
本文给出一个迭代序列强收敛到Ｔ的唯一不动点，并给出一个涉及Ｌｉｐｓｃｈｉｔｚ局部强增殖映象Ｔ的非线性方程Ｔｘ＝ｆ的解的迭代逼近。
3) image enhancement
映象增强
4) local enhancement
局部增强
1.
An adaptive local enhancement algorithm is proposed in this paper, and the evaluation function is defined by edge numbers, edge intensity and the entropy.
根据声图像特点,定义了局部增强函数,以边缘数目、边缘强度和熵定义了评价函数,通过采用PSO算法对增强函数中的参数进行了优化选取。
5) strongly accretive mapping
强增生映象
1.
Under the only assumption of the continuity of strongly accretive mappings, and by virtue of the Holder continuity of th duality mapping Jp given by Prof.
在仅假设强增生映象的连续性下,利用徐宗本教授等人(1991年)给出的对偶映象Jp的Holder连续性,证明了具误差的Mann迭代法强收敛到这类变分包含的唯一解。
2.
Under the only assumption of the continuity of strongly accretive mappings and without the condition βn→0(n→∞), by virtue of the Holder continuity of the duality mapping Jp given by Xu Zong-ben et al.
在仅假设强增生映象的连续性与没有条件βn→0(n→∞)下,利用徐宗本等人给出的对偶映象Jp的Holder连续性,证明了具误差的Ishikawa迭代程序强收敛到这类变分包含的唯一解。
3.
The author presents a related result that the new Ishikawa iterative with errors converges to a solution of the nonlinear equation Tx = f when T is Lipschitz strongly accretive mapping.
本文给出一个新的具误差的 Ishikawa 迭代程序强收敛到 T 的唯一不动点,并给出一个涉 及 Lipschitz 强增生映象 T 的非线性方程 Tx = f 的解的迭代逼近。
6) locality enhancement
局部性增强
补充资料：映象
客观事物在人的头脑中以感觉、观念或思想等形式的再现。是客观事物作用于人脑和人脑进行反映活动的结果。实践是客观事物映象的来源。人脑中关于客观事物的映象，都是在具体实践条件下，人脑对客观事物的近似正确的或错误的反映。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条