Sun Fuyan, Gong Chunyan, Lyu Zongwang
College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China.
Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou, 450001, China.
Sci Rep. 2024 Oct 14;14(1):24015. doi: 10.1038/s41598-024-74120-1.
Grain storage has very strict temperature requirements. Aiming at the problems of nonlinear characteristics and poor prediction accuracy of temperature parameters in grain storage, a combination of chaos theory and enhanced radial basis neural network is proposed as a temperature prediction model for grain storage (C-ERBF). The model first determines the embedding dimension and time delay of the grain storage temperature sequence using chaos theory. It then calculates the Lyapunov exponent to confirm its chaotic properties and reconstructs the sequence in the phase space to extract the hidden dynamic information and structure behind the sequence. Furthermore, the q-Normalized Least Mean Square Fourth (qXE-NLMF) algorithm is designed to enhance the radial basis function (RBF) neural network model for weight updating, to improve its prediction accuracy, and to accelerate the training speed of the model. As verified by the simulation experiments of Mackey-Glass chaotic time series prediction, the enhanced RBF (ERBF) network has faster convergence speed and lower steady-state error compared to the traditional RBF network. Finally, the optimized dataset from chaos theory is input into the model to achieve accurate predictions of grain storage temperature series. The experimental results show that the proposed C-ERBF model has higher prediction accuracy compared to other time series prediction methods. It can realize the grain pile temperature in advance, and take control measures in advance. This proactive approach significantly reduces the consumption of stored grain and prevents issues before they arise.
粮食储存对温度有非常严格的要求。针对粮食储存中温度参数的非线性特性和预测精度差的问题，提出了一种将混沌理论与增强型径向基神经网络相结合的粮食储存温度预测模型（C-ERBF）。该模型首先利用混沌理论确定粮食储存温度序列的嵌入维数和时间延迟。然后计算李雅普诺夫指数以确认其混沌特性，并在相空间中重构序列以提取序列背后隐藏的动态信息和结构。此外，设计了q-归一化最小均方四阶（qXE-NLMF）算法来增强径向基函数（RBF）神经网络模型进行权重更新，提高其预测精度，并加快模型的训练速度。通过Mackey-Glass混沌时间序列预测的仿真实验验证，与传统RBF网络相比，增强型RBF（ERBF）网络具有更快的收敛速度和更低的稳态误差。最后，将来自混沌理论的优化数据集输入模型，以实现对粮食储存温度序列的准确预测。实验结果表明，所提出的C-ERBF模型与其他时间序列预测方法相比具有更高的预测精度。它可以提前实现粮堆温度，并提前采取控制措施。这种主动方法显著减少了储存粮食的消耗，并在问题出现之前进行预防。
