围绕非线性系统的建模问题,提出了一种基于改进型深度学习的非线性建模方法.首先,设计了基于高斯径向基函数的深度信念网络训练模型;其次,利用对比分歧算法对径向基函数的权值、中心和宽度进行调整,并利用反向传播对网络连接权值进行优化;最后,将获得的改进型深度学习方法应用于非线性系统建模.实验结果验证了该算法的有效性和可行性.“,”To model nonlinear systems, we propose a nonlinear modeling method based on an improved deep learning algorithm. First, we design a learning model of a deep belief network based on the Gaussian radial basis function. Second, we adjust the weight, center, and width of the radial basis functions by a contrastive divergence algorithm and optimize the weights of the deep belief network using a back-propagation algorithm. Finally, we apply the improved deep learning algorithm to model nonlinear systems. The experimental results verify the effectiveness and feasibility of the algorithm.