前言:
而今你们对“matlab仿真fft”大致比较关注,各位老铁们都需要知道一些“matlab仿真fft”的相关文章。那么小编同时在网摘上汇集了一些关于“matlab仿真fft””的相关资讯,希望朋友们能喜欢,兄弟们快快来学习一下吧!中国在这波AI热潮中不能落伍!Chat GPT突然掀起的这波AI新高潮,令各国各界各大公司竟然措手不及!虽然对于Chat GPT聊天对话智慧、书写文案水平等有过度夸张炒作之嫌疑,但是Chat GPT此前通过了美国医师执照考试、律师资格考试、工商管理硕士运营考试!业界直呼“知识型员工应该非常担心自己的岗位了!”Chat GPT“ 考过这三个准入资格是实实在在的。
下面我们就用这个chatGPT开始编程之旅,首先我们用chatGPT完成FFT代码的编程和测试,首先是将chatGPT生成的代码在Matlab中进行验证(下一节将 再利用生的代码转为C 代码在平台上进行研证)。
第一步我们首先让chatGPT为我们生成FFT的代码,代码将限定从一个频率到另一个特定频率的数据进行检查,这样将降低CPU的使用率,对于在部分资源有限的平台上跑FFT算法是非常有益的:
The function takes four arguments: signal is the input signal, fs is the sampling frequency, f1 is the lower frequency bound, and f2 is the upper frequency bound. It returns two output variables: freq is the frequency vector between f1 and f2, and mag is the magnitude spectrum of the signal between f1 and f2.
The function works by computing the one-sided FFT of the signal using the built-in fft function, and then extracting the frequency and magnitude vectors between f1 and f2. It returns these vectors as output variables.
You can call this function from your own MATLAB code to compute the FFT of a signal between a lower and upper frequency bound, like this:
% Generate test signalfs = 1000; % Sampling frequencyt = 0:1/fs:1-1/fs; % Time vectorf1 = 40; % Lower frequency boundf2 = 120; % Upper frequency boundsignal = sin(2*pi*50*t) + 0.5*sin(2*pi*100*t) + 0.2*randn(size(t)); % Signal with noise% Compute FFT between f1 and f2[freq, mag] = fft_between_freqs(signal, fs, f1, f2);% Plot the magnitude spectrumplot(freq, mag);xlabel('Frequency (Hz)');ylabel('Magnitude');title(sprintf('FFT between %d and %d Hz', f1, f2));
In this example, the function is called with a test signal consisting of a sine wave with frequency 50 Hz and a noisy sine wave with frequency 100 Hz, with a sampling rate of 1000 Hz. The function is asked to compute the FFT between 40 and 120 Hz, and the resulting magnitude spectrum is plotted.
copy以上代码到Matlab 上进行验证:
代码测试结果如图:
修改关注的频率由40HZ-120HZ 到 40HZ-100HZ, 看运行结果:
综上所术用chatGPT生成的Matlab代码和数据仿真基本上能够满足我们的要求, 同时我们也可以把范围缩小,运算的速度会更快,不用关心所有频率范围。在下面的文章中我们将把chatGPT中生的代码,运用到嵌入式产品中,看实际运行的结果能否满足产品需求。
标签: #matlab仿真fft #fft的matlab程序