The matrix extension problem is, under some constrained conditions, constructing a matrix A with a given matrix A_0 as its submatrix.
矩阵扩充问题是在某种约束条件下构造矩阵A，使得矩阵A的一个子矩阵为A_0。
The problem of matrix extension is to construct matrices with a given matrix or a several given matrices as its sub-matrices under some constrained conditions.
矩阵扩充问题是当给定一个或多个子矩阵，在某种约束条件下构造矩阵的问题。
Extension matrix is an approach to induce rules.
扩张矩阵是一种规则归纳的方法。
Due to introduce of the fuzzy idea, the fuzzy extension matrix can deal with uncertainty associated with human thinking and perception, so it is used more and more widely.
而模糊扩张矩阵是在扩张矩阵基础上引入了模糊思想，使之能处理与人的思维和感觉有关的不确定性数据，因而得到了广泛的应用。
This paper discussed the path of extension matrix and introduced a new heuristic algorithm.
本文讨论了扩张矩阵的路径，提出了一种抽取模糊规则的启发式算法。
A new type cumulant-based virtual DOA matrix is formed using the property of aperture extension of fourth-order cumulants.
利用空间四阶累量的孔径扩展性质，构造了一种新的累量域虚拟阵列波达方向矩阵。
Up to now, some heuristic algorithms have been proposed for learning from examples based on extension matrix theory.
到目前为止，一些启发式算法被提出用于基于扩张矩阵理论的示例学习研究。
A computational theory of learning from examples, extension matrix theory, is presented.
本文提出示例学习的一种计算理论，扩张矩阵论。
Under fuzzy environment, the fuzzy extension matrix approach can generate a set of fuzzy rules from examples according to the minimum fuzzy entropy criterion of the path.
在模糊环境下，模糊扩张矩阵算法根据路径的最小模糊信息熵标准，从示例中归纳产生一组模糊规则。
We propose a new approach to generating optimum intrusion detection rules based on extension matrix and Genetic Algorithm(GA)theory.
提出了一种新的基于扩张矩阵和遗传算法理论产生最优检测规则的方法。
Also, after some prelimenaries, We have Obtained that the semigroup is further nil-extension of the matrix of groups, but the converse is not all true.
在给出它的若干特征之后，指出这一类半群也是群的矩阵的幂零元-理想扩张，但反之未必成立。
Compared with the crisp extension matrix, the proposed method has the capability of handling fuzzy representation and tolerating noisy data or missing data.
与清晰情况下相比较，我们所实现的这种算法能够处理模糊数据，对噪音数据和不完整数据有很好的鲁棒性。
Compared with the crisp extension matrix, the proposed method has the capability of handling fuzzy representation and tolerating noisy data or missing data.
与清晰情况下相比较，我们所实现的这种算法能够处理模糊数据，对噪音数据和不完整数据有很好的鲁棒性。
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