Derivative property of the Fourier transform

f ( t ) f(t) f(t) has the property:

  1. The domain is ( ∞ , − ∞ ) (\infty, -\infty) (,)
  2. The integral of f ( t ) f(t) f(t) is limited
    ∫ − ∞ ∞ f ( t ) d t = A \int ^{\infty} _{-\infty}f(t)dt= A f(t)dt=A
    and
    lim ⁡ t → ∞ f ( t ) = 0 { \lim_{t \to \infty} f(t)=0} tlimf(t)=0
    The Fourier transform of f ( t ) f(t) f(t)

f ( t ) → F ( ω ) f(t) \rightarrow F(\omega) f(t)F(ω)
is
F ( ω ) = ∫ − ∞ ∞ f ( t ) e x p ( − j ω t ) d t F(\omega)=\int ^{\infty} _{-\infty}f(t)exp(-j\omega t)dt F(ω)=f(t)exp(jωt)dt
What is about the Fourier transform of the first derivative of f ( t ) f(t) f(t)?
Let’s use the definition
f ′ ( t ) → F ( ω ) f^{\prime}(t) \rightarrow F(\omega) f(t)F(ω)
F ( f ′ ( t ) ) = ∫ − ∞ ∞ f ′ ( t ) e x p ( − j ω t ) d t \mathcal{F}\left (f^{\prime}(t) \right)=\int ^{\infty} _{-\infty}f^{\prime}(t) exp(-j\omega t)dt F(f(t))=f(t)exp(jωt)dt
Integral by part
F ( ω ) = f ( t ) e x p ( − j ω t ) ∣ − ∞ ∞ − ∫ − ∞ ∞ f ( t ) ( e x p ( − j ω t ) ) ′ d t F(\omega)= f(t)exp(-j\omega t) \bigg |^{\infty}_{-\infty}-\int ^{\infty} _{-\infty}f(t) \left ( exp(-j\omega t) \right) ^{\prime} dt F(ω)=f(t)exp(jωt)f(t)(exp(jωt))dt
= f ( t ) e x p ( − j ω t ) ∣ − ∞ ∞ + j ω ∫ − ∞ ∞ f ( t ) e x p ( − j ω t ) d t = f(t)exp(-j\omega t) \bigg |^{\infty}_{-\infty} + j\omega \int ^{\infty} _{-\infty}f(t) exp(-j\omega t) dt =f(t)exp(jωt)+jωf(t)exp(jωt)dt
= f ( t ) e x p ( − j ω t ) ∣ − ∞ ∞ + j ω ∫ − ∞ ∞ f ( t ) e x p ( − j ω t ) d t = f(t)exp(-j\omega t) \bigg |^{\infty}_{-\infty} + j\omega \int ^{\infty} _{-\infty}f(t) exp(-j\omega t) dt =f(t)exp(jωt)+jωf(t)exp(jωt)dt
= j ω ∫ − ∞ ∞ f ( t ) e x p ( − j ω t ) d t = j\omega \int ^{\infty} _{-\infty}f(t) exp(-j\omega t) dt =jωf(t)exp(jωt)dt
Therefore,
F ( f n ( t ) ) = ( j ω ) n ∫ − ∞ ∞ f ( t ) e x p ( − j ω t ) d t \mathcal{F}(f^n(t))= (j\omega)^n \int ^{\infty} _{-\infty}f(t) exp(-j\omega t) dt F(fn(t))=(jω)nf(t)exp(jωt)dt

### 导数的循环方差概念及其计算 在数值分析和统计学中,导数的循环方差(Cyclic Variance)并不是一个标准术语。然而,可以推测这一概念可能是指对周期性数据或时间序列中的导数变化率进行方差分析。 #### 定义与解释 假设有一个周期函数 \( f(t) \),其一阶导数表示为 \( f'(t) \)。对于该导数的时间序列,可以通过以下方式定义循环方差: \[ V_c(f') = \frac{1}{N} \sum_{i=1}^{N}(f'_i - \bar{f'})^2 \] 其中, - \( N \) 是样本数量; - \( f'_i \) 表示第 i 个时刻的一阶导数值; - \( \bar{f'} \) 表示整个时间段内所有导数值的平均值; 此公式用于衡量一段时间窗口内的波动程度,特别适用于具有重复模式的数据集[^3]。 #### 计算过程展示 下面是一个简单的Python实现来说明如何计算给定周期信号的一阶导数并求解对应的循环方差: ```python import numpy as np def cyclic_variance_derivative(signal, period): """ Calculate the derivative of a signal and its cyclic variance. Parameters: signal (list/array): Input time series data points. period (int): Periodicity length within the dataset. Returns: float: Cyclic variance value for derivatives. """ # Compute first-order differences approximating derivatives diff_signal = np.diff(signal) # Ensure we only consider complete cycles when computing mean & variances n_cycles = int(len(diff_signal)/period) truncated_diffs = diff_signal[:n_cycles*period].reshape(-1, period) means_per_cycle = np.mean(truncated_diffs, axis=0) overall_mean = np.mean(means_per_cycle) # Finally compute the cyclic variance based on deviations from cycle-wise averages cvd = np.sum((means_per_cycle - overall_mean)**2)/(len(means_per_cycle)-1) return cvd # Example usage with synthetic sine wave plus noise time_points = np.linspace(0, 8*np.pi, num=500) noisy_sine_wave = np.sin(time_points)+np.random.normal(scale=.1,size=len(time_points)) cvd_result = cyclic_variance_derivative(noisy_sine_wave.tolist(), 50) print("Calculated Cyclic Variance:", cvd_result) ``` 上述代码片段展示了如何针对特定周期长度的时间序列执行一次完整的操作流程,包括获取离散化后的近似导数以及最终得出相应的循环方差指标。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值