高层建筑结构设计BP算法的精度优化

Accuracy Optimization of BP Algorithm for High Rise Building Structure Design

针对传统的BP神经网络算法在对高层建筑进行结构设计时还存在精度不高、误差较大等问题,本文提出了一种基于自适应和误差修正BP神经网络算法的高层建筑结构设计模型,该模型在BP神经网络算法的基础上,首先采用自适应调整策略对其网络模型进行优化,然后采用增加动量项、误差累积处理和陡度因子优化等误差修正策略提高原算法的训练精度。仿真试验结果表明,本文提出的基于自适应和误差修正BP神经网络算法的高层建筑结构设计模型相比较传统的BP神经网络算法精度要高,具有较好的鲁棒性。

According to the defects such as low accuracy and large error of the traditional BP neural network algorithm in the design of high-rise building structure, this paper presents a high-rise building structure design model based on BP neural network algorithm with adaptive and error correction. The model based on BP neural network algorithm, first network model is optimized by the adaptive adjustment strategy, and then the error correction strategies such as increasing momentum item and cumulative error handling and steepness factor optimization are used to improve the training precision of the original algorithm. Simulation test results show that, compared with the traditional BP neural network algorithm, the proposed high-rise building structure design model based on BP neural network algorithm with adaptive and error correction has higher precision and good robustness.