泛函网络在线增量式学习算法及应用

Online Incremental Learning Algorithm and Application to Functional Networks

给出了一类泛函网络的数学模型,并分析了它的拓扑结构特点和离线学习过程。在此基础上根据分块矩阵计算方法和泛函网络基函数矩阵本身的特点,给出了泛函网络的两种在线增量式学习算法。该算法能充分利用历史训练结果,具有学习、修正和应变功能。最后,以H(?)non时间序列为例进行仿真。仿真结果表明这两种学习算法是可行和有效的。

A mathematical model of functional networks is proposed. The property of its topology structure and learning process is analyzed. Online incremental learning algorithms based on the block matrix and the property of functions matrix are designed. The learning algorithms make fully use of the training history, and have functions of learning, modification and emergency adaptation. Simulation on a Henon time series shows effectiveness of the proposed algorithms.