针对目前绝对式编码器人工调试输出信号精度不稳定的情况，现设计一套输出信号误差自动补偿系统。系统通过TCP 与服务器通信，获得待补偿参数。分析信号产生误差的原因，建立径向基神经网络模型，以高精度编码器检测值作为学习目标，将形成最小误差的输出值作为精调的理想输入值，实现误差最小化，校准数据后写入编码器芯片。实验证明，使用自动补偿系统调试后的编码器各码道幅值、相位和偏移量，相较于人工手动补偿，信号误差平均减小了49.82%。

In view of this phenomenon that the output signal precision of the absolute type encoder is not stable, which relies on artificial debugging, a set of output signal error automatic compensation system is designed. The system establishes the connection with the server by TCP communications, and acquired the parameters needing to debug. Then analyze the reason for leading to the signal error. With the high-precision encoder estimated value as the goal of learning, it establishes Radial Basis Function neural network mode, which would form the output value of the minimum error as the ideal input value of the fine adjustment. In this way, it might realize errors minimizing. After the calibration data, write to the encoder chip. Experiments show that, the automatic system is used to compensate each code channel of the value of amplitude, phase and offset, which compared with the manual debugging, and the signal error is reduced on average by 49.82%.