摘要
∑-π型神经网络是由具有高阶神经网络特性的单元构成的无隐层网络。研究了∑-π型神经网络中的误差曲面和学习效率,给出了理论推导和分析。对于典型的XOR问题,分别使用BP网络和∑-π网络进行了计算机模拟计算。结果表明,BP网络中误差曲面存在大量的平台(flat)和梯阶(stair-step),∑-π网络中误差曲面变化梯度较大,因此具有较快的学习收敛速率,能克服BP网络的不足。按文中所用模拟计算方法,可以建立∑-π网络和BP网络中学习参数与学习效率、误差曲面特性之间关系数据库,有利于∑-π网络和BP网络的参数优选、加速收敛和硬件实现。还讨论了∑-π网络和BP网络的神经生物学基础。
?π neural network is a no-hidden-layer network formed by the units with properties of higher-order neural network model. In the present study, the author investigated the error surfaces and learning efficiency in ∑-π neural network, gave the theoretical analyses and studied XOR problem by means of computer simulation calculations using BP network and ∑-π network separately to compare the 2 networks. The results s ow the divergence of error surfaces in ∑-π network is greater than that in BP network and there is no stair-step and flat, which often appear in BP network. Therefore ∑-π network, with faster convergence rate, can compement the shortcoming of BP network. We can use these calculation methods to build a data bank to relate learning parameters, to learning efficiency and error surfaces, so as to optimize the parameters in the 2 networks and help speed convergence and the realization of hardware. The neurobiological bases of BP and ∑-π networks are discussed.
出处
《徐州医学院学报》
CAS
1995年第2期137-141,共5页
Acta Academiae Medicinae Xuzhou