一种回归神经网络的快速在线学习算法
A New on-Line Recursive Learning Algorithm for Recurrent Neural Network
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摘要: 针对回归神经网络BP学习算法收敛慢的缺陷,提出了一种新的快速在线递推学 习算法.本算法在目标函数中引入了遗忘因子,并借助于非线性系统的最大似然估计原理成 功地解决了动态非线性系统回归神经网络模型权系数学习的实时性和快速性问题.仿真结果 表明,该算法比传统的回归BP学习算法具有更快的收敛速度.Abstract: In this paper a new on-line recursive learning algorithm for recurrent neural network is proposed. It overcomes the disadvantage of the slow convergence of the recurrent BP algorithm. The real-time learning ability and the fast convergence of the recurrent network model of nonlinear dynamical system have been obtained by introducing the forgetting factor in the objective function and the maximum likelihood estimation principle. Simulation results show that the proposed algorithm performs better than the traditional recurrent BP algorithm.
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Key words:
- Recurrent neural network /
- on-line learning /
- fast algorithm
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