Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse.

在这项研究中，与为动态问题设计的传统张神经网络（ZNN）相比，变参数收敛加速神经网络（VPCANN）模型被通用化以解决动态矩阵伪逆，该矩阵可以实现超指数收敛和抗噪声性。 。仿真实验表明，VPCANN合成的神经状态解可以快速逼近理论上的伪逆。此外，基于恒定噪声，随机噪声和动态噪声三种类型的噪声干扰，对VPCANN模型与ZNN模型进行了比较，验证了VPCANN模型的抗噪声性能优于ZNN。另外，为了展示VPCANN在实践中的潜在应用，我们考虑了六连杆机械手的运动学运动计划，