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针对目前的自适应结构不利于Hammerstein预失真器进行参数更新,尤其是不利于线性子系统参数更新的问题,该文在间接学习结构的基础上提出了一种改进的自适应预失真结构。应用该结构能够得到Hammer-stein预失真器中待识别线性子系统的误差,使得高效的最小二乘算法能够直接对其参数进行更新,进而提高了整个预失真器的线性化性能。仿真结果表明：应用文中自适应结构,Hammerstein预失真器可以高效地补偿带记忆效应功率放大器的非线性失真。“,”The existing adaptive structure are not suitable to update the parameters for Hammerstein predistorter,especially for the linear subsystem.In order to solve the problem,an improved adaptive structure based on indirect learning structure is proposed in this paper.Efficient least-square algorithm can be used to update parameters of the linear subsystem in a Hammerstein predistorter by this adaptive structure which can provide the error of the linear subsystem.Therefore,the linearization efficiency of the whole predistorter is improved.The experimental results show that the Hammerstein predistorter by using the proposed adaptive structure can compensate the nonlinear distortion of PA with memory effects efficiently.