将模糊CMAC（小脑模型关节控制器）神经网络用于一类单输入单输出（SISO）连续时间非线性系统的反馈线性化。控制器由基于两个模糊CMAC神经网络的状态反馈线性化部分和一个用于使控制幅度有界的鲁棒控制部分所组成．在很弱的假设条件下，应用李雅普诺夫稳定性理论严格地证明了闭环系统内的所有信号为UUB（一致最终有界）．本方法特别适合于实时控制． The Fuzzy CMAC (Cerebellar Model Joint Controller) Neural Network is Applied to Feedback Linearization of a Class of Single Input Single Output (SISO) Continuous Time Nonlinear Systems. The controller consists of a state feedback linearization section based on two fuzzy CMAC neural networks and a robust control section for bounded control amplitude. Under very weak assumptions, applying the Lyapunov stability theory, all signals in the closed-loop system are strictly proved to be UUB (uniformly final bounded). This method is particularly suitable for real-time control.