A constructive-pruning hybrid method （CPHM） for radial basis function （RBF） networks is proposed to improve the prediction accuracy of ash fusion temperatures （AFT）. The CPHM incorporates the advantages of the construction algorithm and the pruning algorithm of neural networks, and the training process of the CPHM is divided into two stages： rough tuning and fine tuning. In rough tuning, new hidden units are added to the current network until some performance index is satisfied. In fine tuning, the network structure and the model parameters are further adjusted. And, based on components of coal ash, a model using the CPHM is established to predict the AFT. The results show that the CPHM prediction model is characterized by its high precision, compact network structure, as well as strong generalization ability and robustness.
详细介绍了一种基于COM技术的Delphi和Matlab混合编程的方法。利用Matlab COM Builder将Matlab函数文件转换为COM组件,在Delphi程序中调用该组件,并通过对新型煤场煤堆三维数据进行曲面拟合的实例说明此方法。Matlab具有强大的图形处理能力,Delphi具有友好的用户界面,将二者有机结合,可快速高效地开发出功能强大的应用软件。
