基于交替方向乘子法的球磨机负荷分布式随机权值神经网络模型

Distributed Random Weight Neural Network Model for Ball Mill Load Based on Alternating Direction Multiplier Method

针对传统集中式机器学习处理大规模数据存在通信开销大、算法时间和空间复杂度高等问题,基于交替方向乘子法(ADMM),提出一种球磨机负荷分布式随机权值神经网络建模方法,局部网络节点采用正则化随机权值功能连接RVFL网络,全局球磨机负荷模型参数采用分布式优化学习ADMM方法交替迭代更新求解。实验结果表明,基于ADMM-RVFL的球磨机负荷模型在计算速度和精度方面具有相对优越性。

When the traditional centralized machine learning algorithms deal with the large-scale data, there exists high communication overhead, low computational efficiency and large space complexity. A distributed random weights neural network modeling method is used to build ball mill load model based on Alternate Direction Multiplier Method (ADMM). Local network nodes are built using Random Vector Functional-Link (RVFL) network with regularized random weights, and the parameters of global distributed ball mill load model are optimized iteratively to update the solution by using the ADMM method. The experimental results show that the ADMM-RVFL-based ball mill load model has comparative advantages in terms of speed and accuracy.