给出一种求解一元多项式的最大公因式新方法。
A new method to solve the greatest common factor of one variable polynomials is proposed.
给出一些特殊的整环与它的一元多项式环之间的关系。
In this paper we analyze the relation of some special domains and its monadic polynomial rings.
利用矩阵的秩给出一元多项式整除性的判定定理，同时给出商式的简便求法。
This text that make use of the order of matrix gives outs the unitary polynomials divide exactly theorem of judgment; And give a simple and convenient method about quotients type at the same time.
本文确定了任意格上一元多项式和二元多项式的结构，并给出了三元多项式的几个结果。
The structure of unary and binary polynomials over a lattice is determined in this paper.
本文确定了任意格上一元多项式和二元多项式的结构，并给出了三元多项式的几个结果。
The structure of unary and binary polynomials over a lattice is determined in this paper. Moreover, some remarks on trinary polynomials are offered.
其中一元多项式矩阵的性质已经归纳出很多，但是对二元乃至多元多项式矩阵性质的探讨仍旧很少。
The property of polynomial of one indeterminate matrix has been summed up a lot, but polynomial in two elements and multivariate polynomial of the matrix are discussed a little.
利用数域上一元多项式环与整数环相似的性质，建立数域上一元多项式环中的孙子定理，并给出它的简单应用。
Using the similarity between polynomial ring and integer ring the paper establishes Sunse's Theorem in the polynomial ring in number field, and offers its brief learning and practice.
这种方法能够保证辨识出的参数是最佳的；而且不用求解对应的非线性最小二乘问题，只需求一元多项式的根，从而大大减少计算量。
This approach can guarantee that the identified parameters are optimal, by solving a one-dimensional polynomial equation instead of a nonlinear least square problem.
提出了一元多项式形式的水蒸气饱和压力与饱和温度关系的数学模型，进行了牛肉冻结过程的计算机模拟，在此基础上进行了牛肉冻结过程中干耗量的数值计算。
In this paper, the mathematical model of the reltion between water saturation vapor pressure and saturated temperature is established in the form of monoacidic polynomials.
一元稀疏多项式的表示及运算。
第六部分采用一元线性回归、多项式回归、灰色系统G（1，1）模型和组合预测模型对山东省现代物流业未来需求状况进行了中期预测。
The sixth part forecasts the future logistics demand of Shandong Province on the basis of one linear regression, polynomial regression, gray system G(1,1) model and combination forecast model.
第六部分采用一元线性回归、多项式回归、灰色系统G（1，1）模型和组合预测模型对山东省现代物流业未来需求状况进行了中期预测。
The sixth part forecasts the future logistics demand of Shandong Province on the basis of one linear regression, polynomial regression, gray system G(1,1) model and combination forecast model.
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