sqr_cube.c

  name="google_ads_frame" marginwidth="0" marginheight="0" src="http://pagead2.googlesyndication.com/pagead/ads?client=ca-pub-5572165936844014&dt=1194442938015&lmt=1194190197&format=336x280_as&output=html&correlator=1194442937843&url=file%3A%2F%2F%2FC%3A%2FDocuments%2520and%2520Settings%2Flhh1%2F%E6%A1%8C%E9%9D%A2%2FCLanguage.htm&color_bg=FFFFFF&color_text=000000&color_link=000000&color_url=FFFFFF&color_border=FFFFFF&ad_type=text&ga_vid=583001034.1194442938&ga_sid=1194442938&ga_hid=1942779085&flash=9&u_h=768&u_w=1024&u_ah=740&u_aw=1024&u_cd=32&u_tz=480&u_java=true" frameborder="0" width="336" scrolling="no" height="280" allowtransparency="allowtransparency"> #include <stdio.h>

#define SQUARE(x) ((x) * (x))
#define CUBE(x) ((x) * (x) * (x))

void main(void)
 {
   printf("The square of 2 is %d/n", SQUARE(2));
   printf("The cube of 100 is %f/n", CUBE(100.0));
 }

 

#include "stm32f4xx_hal.h" // Device header #include "ADC.h" #include "stdio.h" ADC_HandleTypeDef hadc1; DMA_HandleTypeDef hdma_adc1; /* ADC1 init function */ void MX_ADC1_Init(void) { ADC_ChannelConfTypeDef sConfig = {0}; hadc1.Instance = ADC1; hadc1.Init.ClockPrescaler = ADC_CLOCK_SYNC_PCLK_DIV2; hadc1.Init.Resolution = ADC_RESOLUTION_12B; hadc1.Init.ScanConvMode = ENABLE; hadc1.Init.ContinuousConvMode = ENABLE; hadc1.Init.DiscontinuousConvMode = DISABLE; hadc1.Init.ExternalTrigConvEdge = ADC_EXTERNALTRIGCONVEDGE_NONE; hadc1.Init.ExternalTrigConv = ADC_SOFTWARE_START; hadc1.Init.DataAlign = ADC_DATAALIGN_RIGHT; hadc1.Init.NbrOfConversion = 3; hadc1.Init.DMAContinuousRequests = ENABLE; hadc1.Init.EOCSelection = ADC_EOC_SINGLE_CONV; if (HAL_ADC_Init(&hadc1) != HAL_OK) { printf("OERRRO1\r\n"); } /** Configure for the selected ADC regular channel its corresponding rank in the sequencer and its sample time. */ sConfig.Channel = ADC_CHANNEL_9; sConfig.Rank = 1; sConfig.SamplingTime = ADC_SAMPLETIME_144CYCLES; if (HAL_ADC_ConfigChannel(&hadc1, &sConfig) != HAL_OK) { printf("OERRRO2\r\n"); } sConfig.Channel = ADC_CHANNEL_10; sConfig.Rank = 2; sConfig.SamplingTime = ADC_SAMPLETIME_144CYCLES; if (HAL_ADC_ConfigChannel(&hadc1, &sConfig) != HAL_OK) { printf("OERRRO3\r\n"); } /** Configure for the selected ADC regular channel its corresponding rank in the sequencer and its sample time. */ sConfig.Channel = ADC_CHANNEL_15; sConfig.Rank = 3; sConfig.SamplingTime = ADC_SAMPLETIME_144CYCLES; if (HAL_ADC_ConfigChannel(&hadc1, &sConfig) != HAL_OK) { printf("OERRRO4\r\n"); } } void HAL_ADC_MspInit(ADC_HandleTypeDef* adcHandle) { GPIO_InitTypeDef GPIO_InitStruct = {0}; if(adcHandle->Instance==ADC1) { /* ADC1 clock enable */ __HAL_RCC_ADC1_CLK_ENABLE(); __HAL_RCC_GPIOC_CLK_ENABLE(); __HAL_RCC_GPIOB_CLK_ENABLE(); /**ADC1 GPIO Configuration PC5 ------> ADC1_IN15 PB1 ------> ADC1_IN9 */ GPIO_InitStruct.Pin = GPIO_PIN_5|GPIO_PIN_0; GPIO_InitStruct.Mode = GPIO_MODE_ANALOG; GPIO_InitStruct.Pull = GPIO_NOPULL; HAL_GPIO_Init(GPIOC, &GPIO_InitStruct); GPIO_InitStruct.Pin = GPIO_PIN_1; GPIO_InitStruct.Mode = GPIO_MODE_ANALOG; GPIO_InitStruct.Pull = GPIO_NOPULL; HAL_GPIO_Init(GPIOB, &GPIO_InitStruct); /* ADC1 DMA Init */ /* ADC1 Init */ hdma_adc1.Instance = DMA2_Stream0; hdma_adc1.Init.Channel = DMA_CHANNEL_0; hdma_adc1.Init.Direction = DMA_PERIPH_TO_MEMORY; hdma_adc1.Init.PeriphInc = DMA_PINC_DISABLE; hdma_adc1.Init.MemInc = DMA_MINC_ENABLE; hdma_adc1.Init.PeriphDataAlignment = DMA_PDATAALIGN_HALFWORD; hdma_adc1.Init.MemDataAlignment = DMA_MDATAALIGN_HALFWORD; hdma_adc1.Init.Mode = DMA_CIRCULAR; hdma_adc1.Init.Priority = DMA_PRIORITY_LOW; hdma_adc1.Init.FIFOMode = DMA_FIFOMODE_DISABLE; printf("HAL_ADC_MspInit\r\n"); if (HAL_DMA_Init(&hdma_adc1) != HAL_OK) { } __HAL_LINKDMA(adcHandle,DMA_Handle,hdma_adc1); /* USER CODE BEGIN ADC1_MspInit 1 */ /* USER CODE END ADC1_MspInit 1 */ } } /* USER CODE BEGIN 1 */ /* USER CODE END 1 */ 代码修改无法采集通道10和15的数值,采集出来等于0stm32f301 cubemx adc dma
06-10
基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制方法。通过结合数据驱动技术与Koopman算子理论,将非线性系统动态近似为高维线性系统,进而利用递归神经网络(RNN)建模并实现系统行为的精确预测。文中详细阐述了模型构建流程、线性化策略及在预测控制中的集成应用,并提供了完整的Matlab代码实现,便于科研人员复现实验、优化算法并拓展至其他精密控制系统。该方法有效提升了纳米级定位系统的控制精度与动态响应性能。; 适合人群:具备自动控制、机器学习或信号处理背景,熟悉Matlab编程,从事精密仪器控制、智能制造或先进控制算法研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①实现非线性动态系统的数据驱动线性化建模;②提升纳米定位平台的轨迹跟踪与预测控制性能;③为高精度控制系统提供可复现的Koopman-RNN融合解决方案; 阅读建议:建议结合Matlab代码逐段理解算法实现细节,重点关注Koopman观测矩阵构造、RNN训练流程与模型预测控制器(MPC)的集成方式,鼓励在实际硬件平台上验证并调整参数以适应具体应用场景。
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