4SPRR-SPR parallel robot is a novel closed-loop mechanism. The research of kinematics is the basis of real-time and robust robot control. This paper aims to proposing a method to address a surrogate model of forward kinematics for PMs (Parallel Mechanisms). Herein, the forward kinematics model is derived by training the VQTAM (Vector-Quantified Temporal Associative Memory) network, which originates from a SOM (Self-Organized Mapping). During the processes of training, testing and estimating this neural network, the priority K-means tree search algorithm is utilized, thus improving the training efficacy. Furthermore, LLR (Local Linear Regression), LWR (Local Weighted Linear Regression) and LLE (Local Linear Embedding) algorithms are respectively combined with VQTAM to get three improvement algorithms, aiming to further optimize the prediction accuracy of the networks. To speed up solving the least squared equation in the three algorithms, SVD (Singular Value Decomposition) is introduced. Finally, Data from inverse kinematics by geometric method is obtained, which is for constructing and validating the VQTAM neural network. Results show that the prediction effect of LLE algorithm is better than others, which could be a potential surrogate model to estimate the output of forward kinematics.

4SPRR-SPR并联机器人是一种新颖的闭环机构。运动学的研究是实时和鲁棒的机器人控制的基础。本文旨在提出一种解决永磁同步电机（并联机构）正向运动学替代模型的方法。在此，正向运动学模型是通过训练源自SOM（自组织映射）的VQTAM（矢量量化时间关联记忆）网络而得出的。在训练，测试和估计该神经网络的过程中，使用了优先级K均值树搜索算法，从而提高了训练效果。此外，将LLR（局部线性回归），LWR（局部加权线性回归）和LLE（局部线性嵌入）算法分别与VQTAM结合，得到了三种改进算法：旨在进一步优化网络的预测精度。为了加快三种算法中最小二乘方程的求解速度，引入了SVD（奇异值分解）。最后，利用几何方法从逆运动学中获取数据，用于构造和验证VQTAM神经网络。结果表明，LLE算法的预测效果优于其他算法，这可能是估计正向运动学输出的一种潜在替代模型。