Optimizing Turning Parameters Based on Correlation Pruning Neural Networks

The control for turning process was a complicated problem and the suitable turning parameters are instrumental to turning process, the turning parameters optimized with artificial neural networks was proposed in this paper. Artificial neural networks was a non-linear system with strong non-linear modeling ability, but the traditional BP neural networks has many shortcomings like easily step into local minimum, with weak generalization ability and the middle layer neuron are hard to determine, so the correlation pruning algorithm was applied to resolve the problem that the hidden layer nodes of neural networks are hard to determine, the correlation of hidden layer nodes was analyzed and the algorithm was programmed, the simulation shows the methods is effective and can provide a guidance to optimizing turning parameters and turning process control.