Chaotic Time Series Prediction of Multi-Dimensional Transformer Based on Bionic Reconfigurable Three-Dimensional Scanning Robot

With the rapid development of modern science and technology, especially with the emergence and widespread application of computer technology, it has become possible to use computers for predicting chaotic phenomena. There are still some problems in predicting chaotic time series, such as weak robustness, low accuracy, and poor generalization ability. Therefore, this article proposes a new chaos time series prediction algorithm based on the multi-dimensional transformer (DTM) algorithm. This algorithm mainly achieves chaos time series prediction through end-to-end data correlation prediction. Experimental results show that the accuracy of this algorithm reaches 93.227%, which is significantly higher than that of SVM, LSTM and ESN algorithms. Moreover, it has stronger robustness and better generalization performance.