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.
依据最小二乘支持向量机(LS_SVM)的基本理论,针对蓄电池荷电状态(state of charge,SOC)随温度、电压、电流而变化的特点,建立基于LS-SVM支持向量机的蓄电池SOC估测模型。通过数据验证,比较不同核函数下的效果,利用网格搜索寻找最优参数。观察在最优参数和最优核函数下LS_SVM支持向量机的预测效果。结果表明,与其他算法相比,采用RBF核函数,并用网格搜索优化的LS_SVM模型精度较高,适合用在蓄电池的SOC估测上。
