具体写一个基于LMS算法的旁瓣对消代码

好的，下面是基于LMS算法的旁瓣对消代码示例：

import numpy as np

# 定义旁瓣对消器类
class ANC:
    def __init__(self, step_size, taps):
        self.step_size = step_size  # 步长
        self.taps = taps  # 滤波器长度
        self.weights = np.zeros(taps)  # 加权系数初始化为0

    # 更新加权系数
    def update(self, x, d):
        # 添加延迟项
        x_delays = np.zeros(self.taps)
        x_delays[0] = x
        x_delays[1:] = self.x_buffer[:-1]

        # 误差信号
        error = d - np.dot(x_delays, self.weights)

        # 更新加权系数
        self.weights += self.step_size * error * np.conj(x_delays)

        # 更新输入缓存
        self.x_buffer[0] = x
        self.x_buffer[1:] = self.x_buffer[:-1]

    # 处理输入信号
    def process(self, x):
        # 添加延迟项
        x_delays = np.zeros(self.taps)
        x_delays[0] = x
        x_delays[1:] = self.x_buffer[:-1]

        # 计算输出信号
        y = np.dot(x_delays, self.weights)

        # 更新输入缓存
        self.x_buffer[0] = x
        self.x_buffer[1:] = self.x_buffer[:-1]

        return y

if __name__ == '__main__':
    # 测试样例
    x = np.sin(2 * np.pi * 100 * np.arange(0, 1, 1 / 8000))
    noise = np.random.normal(size=len(x))
    d = x + 0.1 * np.sin(2 * np.pi * 500 * np.arange(0, 1, 1 / 8000)) + noise

    # 初始化旁瓣对消器
    taps = 101  # 滤波器长度
    step_size = 0.01  # 步长
    anc = ANC(step_size, taps)
    anc.x_buffer = np.zeros(taps)

    # 处理信号
    y = np.zeros(len(x))
    for i in range(len(x)):
        y[i] = anc.process(d[i])
        anc.update(d[i], x[i])

    # 绘制图形
    import matplotlib.pyplot as plt
    plt.plot(x, label='Original Signal')
    plt.plot(d, alpha=0.5, label='Noisy Signal')
    plt.plot(y, label='Filtered Signal')
    plt.legend()
    plt.show()

希望我的回答能够满足您的需求！