【摘要】 研究了单体模糊神经网络 (MFNNs)的函数逼近能力 .由于在 MFNNs中神经元的基本运算由原来的积 -和运算改为求极小 -极大运算 ,网络的函数逼近性质发生了很大的改变 .给出了单调传递函数的 MFNNs按序单调特性、连续映射定理以及非函数一致逼近定理 .从而说明 MFNNs虽然能够保持连续性映射 ,但不如原神经网络具有函数逼近能力 .更多还原

【Abstract】 This paper deals with function approximation capabilities of monolithic fuzzy neural networks (MFNNs). In MFNNs the basic operators are Min and Max which replace the multiply and sum operators in traditional neural networks. This makes various differences in properties of approximation to function. Proposed in the paper are the ordered monotony property of MFNNs when the transfer function is monotone, the continuous mapping theorem, and non approximation theorem to function. It is shown that although MFNNs can keep continuous mapping property, their capabilities to approximate function is worse comparing with traditional neural networks.更多还原