SeriesCoefficient[series,n]
求出由 Series 产生的幂级数中的 n
次项的系数.
SeriesCoefficient[f,{x,x0,n}]
在 f 关于点
处展开式中
的系数.
SeriesCoefficient[f,{x,x0,nx},{y,y0,ny},…]
求出多元级数的系数.
SeriesCoefficient
SeriesCoefficient[series,n]
求出由 Series 产生的幂级数中的 n
次项的系数.
SeriesCoefficient[f,{x,x0,n}]
在 f 关于点
处展开式中
的系数.
SeriesCoefficient[f,{x,x0,nx},{y,y0,ny},…]
求出多元级数的系数.
更多信息和选项
- 在 SeriesCoefficient[f,{x,x0,n}] 中,次数 n 可以是符号.
- 可以给出下列选项:
-
Assumptions $Assumptions 参数的假设 Method Automatic 可使用的方法 - 对于显式 SeriesData 对象,也可以使用 SeriesCoefficient[series,{nx,ny,…}].
范例
打开所有单元 关闭所有单元基本范例 (4)
Series[Exp[Sin[x]], {x, 0, 10}]SeriesCoefficient[%, 8]SeriesCoefficient[Exp[-x], {x, 0, n}]Table[%, {n, 0, 5}]Series[Exp[-x], {x, 0, 5}]Series[Exp[x + y], {x, 0, 3}, {y, 0, 3}]SeriesCoefficient[%, {3, 3}]SeriesCoefficient[Exp[x + y], {x, 0, n}, {y, 0, m}]范围 (6)
SeriesCoefficient[1 / (x ^ 2 - 3 / 2x + 1), {x, 0, n}]ListPlot[Table[%, {n, 0, 30}], Filling -> Axis]SeriesCoefficient[x / (x - 5), {x, 0, n}]SeriesCoefficient[x ^ 3 / ((-3 + x) (-2 + x) (-1 + x)), {x, 0, n}]SeriesCoefficient[Exp[x] / (1 - x), {x, 0, n}]SeriesCoefficient[Cos[Sqrt[x]], {x, 0, n}]SeriesCoefficient[Log[1 / Sqrt[1 - 5x ^ 2]], {x, 0, n}]SeriesCoefficient[PolyLog[-k, x], {x, 0, n}]SeriesCoefficient[BesselI[3, 2x] / x ^ 7, {x, 0, n}]SeriesCoefficient[HypergeometricPFQ[{1, 2}, {3, 4, 5}, 6x], {x, 0, n}]一般情况下,可能需要用 DifferenceRoot 函数来表示答案:
SeriesCoefficient[BesselI[3, (1/2x + 1)], {x, 0, n}, Assumptions -> n ≥ 0]SeriesCoefficient[Exp[3x + y ^ 2], {x, 0, m}, {y, 0, n}]SeriesCoefficient[Exp[x + y ^ 2] / (x + 1), {x, 0, m}, {y, 0, n}]SeriesCoefficient[Sin[x + y ^ 2] / (x + 1), {x, 0, m}, {y, 0, n}]选项 (3)
Assumptions (2)
SeriesCoefficient[ChebyshevT[k, x], {x, 0, n}]使用 Assumptions 得到一个简单结果:
SeriesCoefficient[ChebyshevT[k, x], {x, 0, n}, Assumptions -> k∈ Integers]没有 Assumptions,产生一般结果:
SeriesCoefficient[Exp[x], {x, 0, n}]有 Assumptions, 给予在假设下的有效结果:
SeriesCoefficient[Exp[x], {x, 0, n}, Assumptions -> (n ≥ 0)]Method (1)
在可能的情况下,产生一个DifferenceRoot 对象:
SeriesCoefficient[Exp[x], {x, 0, n}, Method -> "Holonomic"]应用 (4)
Series[1 / (1 - t - t ^ 2), {t, 0, 10}]SeriesCoefficient[%, 10]Fibonacci[11]Series[1 / (1 - 2 t x + t ^ 2), {t, 0, 4}]SeriesCoefficient[%, 4]ChebyshevU[4, x]GeneratingFunction[y[n + 1] - 5y[n] == 1, n, x]Solve[% /. y[0] -> 2, GeneratingFunction[y[n], n, x]]SeriesCoefficient[GeneratingFunction[y[n], n, x] /. First[%], {x, 0, n}]用 RSolve:
RSolve[{y[n + 1] - 5y[n] == 1, y[0] == 2}, y[n], n]s = SeriesCoefficient[1 / (1 + x), {x, 0, n}]获取 Inactive 形式的幂级数展开:
Inactive[Sum][Refine[s x ^ n, n ≥ 0], {n, 0, Infinity}]flist = {2 / (3 - x), 1 / (x + 10), 3 / (x ^ 2 - x - 2), 1 / (1 + x) ^ 2, 1 / (1 + x) ^ 3, (x ^ 2) / (1 + x) ^ 3, 10 ^ x, (1 - 3 x) ^ (-5), (E ^ x) / x};Grid[Prepend[{#, Inactive[Sum][Refine[SeriesCoefficient[#1, {x, 0, n}] x ^ n, n ≥ 0], {n, 0, Infinity}]}& /@ flist, {"Function", "Power Series"}], IconizedObject[«Grid options»]]//TraditionalForm属性和关系 (4)
用 DiscreteAsymptotic 计算渐近近似式:
DiscreteAsymptotic[Inactive[SeriesCoefficient][-(z / (-1 + z + z ^ 2)), {z, 0, n}],
n -> ∞]Table[Coefficient[Series[Log[1 / Sqrt[1 + 5x ^ 2]], {x, 0, 5}], x, n], {n, 0, 4}]SeriesCoefficient[Log[1 / Sqrt[1 + 5x ^ 2]], {x, 0, n}]Table[%, {n, 0, 4}]CoefficientList 求出级数中的所有系数:
s = Series[1 / (1 - t - t ^ 2), {t, 0, 10}]Table[SeriesCoefficient[s, i], {i, 0, 10}]CoefficientList[s, t]SeriesCoefficient 和 InverseZTransform 密切相关:
SeriesCoefficient[Exp[x ^ 2], {x, 0, n}]InverseZTransform[Exp[x ^ 2] /. x -> 1 / x, x, n]Simplify[% - %%, n ≥ 0]可能存在的问题 (2)
Series[x ^ x, {x, 0, 5}]SeriesCoefficient[%, 4]SeriesCoefficient[EllipticK[Sin[x ^ 2]], {x, 0, n}]Series[EllipticK[Sin[x ^ 2]], {x, 0, 12}]SeriesCoefficient[%, 4]巧妙范例 (2)
SeriesCoefficient[HypergeometricPFQ[{Subscript[a, 1], Subscript[a, 2]}, {Subscript[b, 1], Subscript[b, 2], Subscript[b, 3]}, x], {x, 0, n}]flist = {Exp[x], Sin[x], Cos[x], 1 / (1 + x), Log[1 + x], ArcTan[x]};Grid[Prepend[{#, SeriesCoefficient[#1, {x, 0, n}]}& /@ flist, {f[x], "Series Coefficient"}], IconizedObject[«Grid options»]]//TraditionalForm技术笔记
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▪
- 幂级数 ▪
- 幂级数展开式 ▪
- 幂级数的运算 ▪
- 幂级数的表示 ▪
- 将幂级数转换为普通表达式
相关链接
历史
1996年引入 (3.0) | 在以下年份被更新:2007 (6.0) ▪ 2008 (7.0)
文本
Wolfram Research (1996),SeriesCoefficient,Wolfram 语言函数,https://reference.wolfram.com/language/ref/SeriesCoefficient.html (更新于 2008 年).
CMS
Wolfram 语言. 1996. "SeriesCoefficient." Wolfram 语言与系统参考资料中心. Wolfram Research. 最新版本 2008. https://reference.wolfram.com/language/ref/SeriesCoefficient.html.
APA
Wolfram 语言. (1996). SeriesCoefficient. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/SeriesCoefficient.html 年
BibTeX
@misc{reference.wolfram_2026_seriescoefficient, author="Wolfram Research", title="{SeriesCoefficient}", year="2008", howpublished="\url{https://reference.wolfram.com/language/ref/SeriesCoefficient.html}", note=[Accessed: 29-October-2026]}
BibLaTeX
@online{reference.wolfram_2026_seriescoefficient, organization={Wolfram Research}, title={SeriesCoefficient}, year={2008}, url={https://reference.wolfram.com/language/ref/SeriesCoefficient.html}, note=[Accessed: 29-October-2026]}