对收敛思维的培养与训练需有一定的方法和技巧。
A certain degree of training for convergent thinking need some methods and techniques.
由此得出收敛思维与发散思维是对立统一、相互补充的关系。
So we result convergent thinking and divergent thinking is the unity of opposites, mutually complementary relationship.
而收敛思维要做到对其自身有所超越，收敛与发散必须“合流”。
The convergent thinking has to be beyond their own, convergence and divergence will be "collusion."
创造性思维是开拓人类认识新领域的思维活动，具有多种思维形式，收敛思维是其中重要的一种。
Creative thinking is pioneer of the new field of human knowledge mentality, which has many forms of thinking, and convergent thinking is an important one.
收敛思维分为静态收敛与动态收敛，具有聚焦性、指向性、综合性、程序性、继承性、推理性等特点。
Convergent thinking divided into static and dynamic convergence which has focus, direction and comprehensive, procedures, inheritance, and other characteristics.
收敛思维的误区表现在机械化、僵化的收敛所形成的思维定势，其中较为明显是思维的的心理定势与经验定势。
Convergence thinking errors in the performance of mechanical and rigid formed by the convergence of the set thinking, which is more the Mind-set and experience set obviously.
我的理论是创造性想象的罕见形式是一种超常的由通常不连贯的思维、记忆、感觉、和观念收敛的结果。
My theory is that rare forms of creative imagination are the result of an extraordinary convergence of normally disconnected thoughts, memories, feelings and ideas.
从思维方法上看，要把逻辑思维与非逻辑思维结合起来，把收敛式思维与发散式思维结合起来。
With regard to thinking methodology, fostering creativity requires the combination of logical and non logical thinking and the combination of divergent and convergent thinking.
是一种无方向，有范围，有条理的收敛性思维方式。
Is a kind of direction, the scope of the convergence of structured way of thinking.
方案的研究的过程充分体现了可拓学中的把发散方法与收敛方法结合的思维方式。
These extension control ways based on different character variable represent the method of combined radiate thought and convergent thought in extension logic.
在不考虑状态转移概率的情况下证明了思维进化算法能够收敛到全局最优解。
Based on the model, the convergence of the algorithm is analyzed and the global convergence of the algorithm is proved when leaving the divert probability out of account.
在不考虑状态转移概率的情况下证明了思维进化算法能够收敛到全局最优解。
Based on the model, the convergence of the algorithm is analyzed and the global convergence of the algorithm is proved when leaving the divert probability out of account.
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