基于支持向量机的滤波器设计及硬件实现
发布时间：2018-10-10 11:49
【摘要】：滤波器是电子设备中的常见模块,经典的滤波器设计方法有窗函数法,频率抽取法等。自机器学习的理论出现后,神经网络等算法广泛应用到FIR滤波器的设计中。本文针对传统FIR滤波器设计方法及神经网络设计方法的不足,在改进使用支持向量机(SVM)设计FIR滤波器方法的基础上,提出了 SVM设计FIR滤波器的硬件实现方法,将由SVM设计的滤波器移植到硬件上。使用SVM构造FIR滤波器,得到的滤波器可更新,并且使用的训练样本较少,本文中使用理想滤波器的幅值响应训练SVM。在建立SVM模型的过程中,本文引入针对训练集输出值的放大参数,该参数将数据集分离,并影响最终的幅频响应。SVM模型中训练参数较多,如训练组数、惩罚参数、核函数参数等,本文进行多次测试,将结果进行比较得到最优训练参数,据此构建基于SVM的FIR滤波器模型。相对于窗函数,使用S VM设计的滤波器具有良好的幅频特性,边界控制较为精确,通带较为平缓,阻带波动次数较少,衰减较多。为了保证滤波器的可更改性和便于其移植到其他系统里,利用生成的FIR滤波器模型构建一个位于FPGA上的嵌入式系统。FIR滤波器嵌入式系统主要由SVM构成,对SVM算法中频繁出现的核函数计算以及浮点数乘法加法运算进行硬件实现,对SVM算法中的训练部分和分类部分进行软件框架实现。本文对核函数的硬件实现进行优化,针对RBF核函数,进行算法上的改进,加速运算,同时使用流水线、向量分割等方法加速硬件系统,并平衡速度与资源。最终系统中单次分类测试向量的时间约为20us,滤波准确率可达到98.41%。
[Abstract]:Filter is a common module in electronic equipment. The classical filter design methods include window function method, frequency decimation method and so on. Since the emergence of the theory of machine learning, neural networks and other algorithms are widely used in the design of FIR filters. Aiming at the shortcomings of the traditional FIR filter design method and the neural network design method, this paper proposes a hardware implementation method of SVM design FIR filter based on improving the FIR filter design method using support vector machine (SVM). The filter designed by SVM is transplanted to hardware. Using SVM to construct FIR filter, the filter can be updated and less training samples are used. In this paper, the amplitude response of ideal filter is used to train SVM.. In the process of establishing the SVM model, this paper introduces the amplification parameter for the output value of the training set, which separates the data set and affects the final amplitude-frequency response. There are many training parameters in the SVM model, such as the number of training groups and the penalty parameter. The kernel function parameters are tested several times in this paper, and the optimal training parameters are obtained by comparing the results, and then the FIR filter model based on SVM is constructed. Compared with the window function, the filter designed by S VM has good amplitude-frequency characteristic, the boundary control is more accurate, the passband is more gentle, the frequency of stopband fluctuation is less, and the attenuation is more. In order to ensure the modifiability of the filter and to transplant it to other systems, an embedded system. Fir filter embedded system based on FPGA is constructed by using the generated FIR filter model. The embedded system is mainly composed of SVM. The kernel function calculation and floating-point multiplication addition in SVM algorithm are implemented by hardware, and the training part and classification part of SVM algorithm are implemented by software framework. In this paper, the hardware implementation of kernel function is optimized. For RBF kernel function, the algorithm is improved and the operation is accelerated. At the same time, pipeline and vector partition are used to accelerate the hardware system, and the speed and resources are balanced. In the final system, the time of single classification test vector is about 20us, and the filtering accuracy can reach 98.41%.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN713
本文编号：2261651
[Abstract]:Filter is a common module in electronic equipment. The classical filter design methods include window function method, frequency decimation method and so on. Since the emergence of the theory of machine learning, neural networks and other algorithms are widely used in the design of FIR filters. Aiming at the shortcomings of the traditional FIR filter design method and the neural network design method, this paper proposes a hardware implementation method of SVM design FIR filter based on improving the FIR filter design method using support vector machine (SVM). The filter designed by SVM is transplanted to hardware. Using SVM to construct FIR filter, the filter can be updated and less training samples are used. In this paper, the amplitude response of ideal filter is used to train SVM.. In the process of establishing the SVM model, this paper introduces the amplification parameter for the output value of the training set, which separates the data set and affects the final amplitude-frequency response. There are many training parameters in the SVM model, such as the number of training groups and the penalty parameter. The kernel function parameters are tested several times in this paper, and the optimal training parameters are obtained by comparing the results, and then the FIR filter model based on SVM is constructed. Compared with the window function, the filter designed by S VM has good amplitude-frequency characteristic, the boundary control is more accurate, the passband is more gentle, the frequency of stopband fluctuation is less, and the attenuation is more. In order to ensure the modifiability of the filter and to transplant it to other systems, an embedded system. Fir filter embedded system based on FPGA is constructed by using the generated FIR filter model. The embedded system is mainly composed of SVM. The kernel function calculation and floating-point multiplication addition in SVM algorithm are implemented by hardware, and the training part and classification part of SVM algorithm are implemented by software framework. In this paper, the hardware implementation of kernel function is optimized. For RBF kernel function, the algorithm is improved and the operation is accelerated. At the same time, pipeline and vector partition are used to accelerate the hardware system, and the speed and resources are balanced. In the final system, the time of single classification test vector is about 20us, and the filtering accuracy can reach 98.41%.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN713
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，本文编号：2261651
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