模糊极大极小算子神经元网络的图灵等价性  

Turing Equivalence of Fuzzy Max-Min Operator Neural Networks

作　　者：梁久祯[1] 何新贵[2] 

机构地区：[1]北京航空航天大学计算机科学与工程系 [2]北京系统工程研究所

出　　处：《北京航空航天大学学报》2001年第4期421-424,共4页Journal of Beijing University of Aeronautics and Astronautics

摘　　要：将模糊Zadeh算子的定义域作了扩充 ,并重新定义为模糊极大极小算子 ,使其满足交换律、结合律和零元律 .在此基础上提出一种模糊极大极小算子型神经元网络模型 ,符合一般模糊算子型神经元网络的定义 .与传统的Zadeh算子型模糊神经网络相比具有较强的映射能力 .详细证明了用该模糊极大极小算子神经元网络可以计算与图灵机等价的部分递归函数 ,从而表明模糊极大极小算子神经元网络的计算能力等价于图灵机 .将传统神经元MThe definition region of Zadeh fuzzy operator is extended and the max-min operator is redefined such that it satisfies the exchange law, the combination law and the O-element law. On the above basis, a max-min operator neural network is proposed according with the general definition of fuzzy operator neural networks. Comparing with traditional fuzzy Zadeh operator neural networks, the present network has high mapping ability. It is showed that the max-min operator neural network can compute part-recursion function, which is equivalent to Turing machine. This indicates that fuzzy max-min operator neural network has the same computation ability as Turing machine. This extends the result of Turing equivalence of traditional neural networks of neuron M-P model to fuzzy neural networks.

关 键 词：模糊算子 神经网络 递归函数 图灵等价性 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]