中文摘要：

研究了一种基于动态神经网络支持向量机（SVM）的FPGA硬件实现方法.提出了基于动态神经网络的最小二乘支持向量机（LS-SVM）神经网络结构,完成了VHDL语言描述的基于动态神经网络的LS-SVM结构设计,并在XILINX SPANT3E系列FPGA中完成了LS-SVM的分类与回归实验.结果表明,该硬件实现方法很好地完成了SVM的分类与回归功能,与现有的软件仿真和模拟器件实现相比,该方法具有更快的收敛速度和更高的灵活性.

英文摘要：

A new FPGA hardware implementation approach of dynamic neural network for support vector machines was provided and researched.The structure of dynamic neural network for least square support vector machines（LS-SVM） was proposed.The architecture design of dynamic neural network for LS-SVM based on VHDL language was also performed.The experiments of classification and regression for LS-SVM were achieved on XILINX SPANT3E series FPGA.The experimental results show that it is effective to complete the LS-SVM classification and regression based on presented method.Compared with the（existing） methods based on software implementation or analog device implementation,this approach has（better） convergence rate and better flexibility.