基于连续优化蚁群算法的神经网络在喷射器性能预测中的应用

以现有的喷射器实验数据集作为样本,用单隐层前向神经网络预测喷射器的性能,网络的训练分别采用连续蚁群系统(CACS)算法和连续蚁群优化(ACOR)算法.数值实验结果显示,用这两种蚁群算法所训练的神经网络对于喷射器性能的预测精度能够满足实际工程的要求,其中ACOR算法的训练误差小于一般的BP算法,预测精度也有所提高.

Application of Neural Networks Based on Continuous-ACO Algorithm in Predicting the Performance of Ejectors

A model of the feed forward artificial neural network(ANN)with single hidden layer is established to predict the performance of questioned ejectors,of which a great number of experimental data has been obtained in advance.Two different algorithms of continuous ant colony system(CACS)and ant colony optimization in Rn(ACOR)are adopted,respectively,for network training based on the obtained dataset.Numerical experiments show that the predictions of ejector performance by the ANN trained with the two algorithms can meet the requirement of accuracy in ejector practice,and the training error of ACOR algorithm is smaller than that of conventional BP training algorithm,resulting in higher prediction accuracy.