Convergence Properties Analysis of Gradient Neural Network for Solving Online Linear Equations
-
摘要: 探讨归纳了一类用于在线求解线性方程组的梯度神经网络, 并且证明了该类梯度网络具有全局指数收敛特性, 而非以往提出的渐进收敛特性. 此外, 相对于使用线性激励函数的情况, 当使用幂S形激励函数时网络具有更好的收敛效果. 计算机仿真结果进一步验证了上述分析的准确性和该网络求解线性方程组问题的有效性.Abstract: A gradient neural network (GNN) for solving online a set of simultaneous linear equations is generalized and investigated in this paper. Instead of the earlier-presented asymptotical convergence, global exponential convergence could be proved for such a class of neural networks. In addition, superior convergence could be achieved using power-sigmoid activation-functions, compared with using linear activation-functions. Computer-simulation results substantiate further the above analysis and efficacy of such neural networks.
-
点击查看大图
计量
- 文章访问数: 2497
- HTML全文浏览量: 71
- PDF下载量: 1303
- 被引次数: 0
下载:
