以高维输入神经网络作为生产线产品质量模型

Neural Network Quality Model with High dimension Inputs for Production line Products

探索用高维输入的神经网络对复杂工业生产过程的建模方法。针对网络输入变量维数较高的特点 ,提出一种 BP网络各权重独立训练的分散训练方法。该方法用附加大惯性项来协调各个权重的优化训练 ,运用非线性优化方法调节步长。与用普通的 BP训练方法相比 ,用该方法训练高维输入的 BP网络具有较快的收敛速度和较高的模型精度 ,较好地解决了实际生产过程的产品质量模型问题。

An approach is discussed to establish quality model for complicated industrial process. It presents a learning algorithm that each weight of BP neural network is trained individually. Large inertia term is used in the training of each weight in case against the divergence of the algorithm. Some crucial optimization methods are used to adjust the training step length of each weight. Compared with usual BP training algorithm, this algorithm provides a better convergence rate and the obtained BP neural network has a higher accuracy. An actual neural quality network model is established for a complicated industrial production line.