Aiming at defects of the traditional self-adaptive equalization,a new self-adaptive equalization method based on kernel recursive least-square(KRLS)was proposed.With the proposed method,the original nonlinear data were mapped to a high-dimensional feature space by introducing a kernel function.Then,the standard least-square algorithm was implemented in the high-dimensional feature space.the proposed method was compared with the traditional self-adaptive equalization one.The simulation results showed that the proposed method is supiror to the raditional self-adaptive equalization one;the new method can effectively eliminate the influence of transfer channels.Finally,the proposed method was applied to extraction of weak impact faults in a rotor system,the test results verified the validity of the proposed method.%针对传统的自适应均衡方法存在的不足，提出了一种基于核递推最小二乘（KRLS ）的非线性系统自适应均衡方法。该方法通过引入核函数，将原始的非线性数据映射到高维特征空间，然后在高维特征空间中实施标准最小二乘算法。提出的方法并与传统的非线性系统均衡方法进行了对比分析，仿真研究表明，提出的方法优于传统的均衡方法，能很好的消除传递通道的影响，有效地提取出弱冲击性成分。最后，将提出的方法应用到转子系统的弱冲击性故障提取中，实验结果进一步验证了提出的方法的有效性。
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