蚁群前馈神经网络在煤灰熔点预测中的应用

APPLICATION OF ANT COLONY ALGORITHM AND BP NEURAL NETWORK IN PREDICTION OF COAL ASH FUSION POINT

提出了一种蚁群前馈神经网络模型。采用蚁群算法和BP算法相结合的方法训练神经网络,可避免单纯BP算法容易陷入局部最优的不足,降低算法对初值的敏感性。应用蚁群前馈神经网络建立了灰熔点的模型,并对模型的预测性能进行了验证。结果表明,该方法的预测精度比单一的BP神经网络模型有较大提高,训练后的网络模型可以用于煤灰熔点的预报。

An ant colony- BP neural network model has been put forward. Adopting method combining ant colony with back propagation （BP） algorithm to train the neural network, can overcome the drawback of pure BP algorithm to easily converge on local optima, decreasing the sensitivity of algorithm to the initial value. A model for predicting the coal ash fusion point has been established by using ant colony and BP neural network, and verification of prediction performance for the said model being carried out. Results show that the predicted precision of said method is more enhanced than that of a single BP neural network model, the trained network model can be used for prediction of coal ash fusion point.