Improved ultrasonic differentiation model for structural coal types based on neural network

Tian, Zhen; Wang, Fuzhong; Li, Tao; Bai, Shanshan

doi:10.1016/s1674-5264(09)60038-7

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…11 The PSO is also able to converge to the global optimal solutions with a certain probability. 12 The combination of PSO and BP neural network not only guarantees the learning rate but also solves the weaknesses of BP neural network mentioned former. The potential of the PSO algorithm has been demonstrated by its successful application to optimization problems in artificial neural network design.…”

Section: Introductionmentioning

confidence: 99%

Tool life prediction based on particle swarm optimization–back-propagation neural network

Xue

Wang

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part B:

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As one of the most important factors affecting shop floor management, tool life is determined by the tool flank wear or break, which is related to the tool parameters, cutting conditions and workpiece parameters. It is found that the relationship between these factors and the tool life is too nonlinear to be analytically formulated. For this reason, back-propagation neural network model is used to predict the tool life for its strong ability of nonlinear fitness. To avoid the local optimum, slow convergence and low generalization capability of the back-propagation neural network, a tool life prediction model, which is based on improved particle swarm optimization–back-propagation neural networks, is proposed in this article. The particle swarm optimization is applied to optimize the weights and thresholds of the back-propagation algorithm for improving the ability of global search and generality. Existing sample data of tools are used to train the proposed model for predicting the life of the tools that are similar to the sample data in tool style. The face milling tools and workpiece of 45 steel are selected for experiment. Theoretical analysis and comparative experiments with back-propagation neural network indicate that the life predicted by the particle swarm optimization–back-propagation model is much better than that of the back-propagation model. It proves that particle swarm optimization–back-propagation model has better convergence, stronger robustness and higher generality. This model also provides a theoretical basis for the economization of tool demand analysis and production planning.

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Section: Introductionmentioning

confidence: 99%