【摘要】 忆阻器以其独特的非易失性、天然的记忆功能以及纳米级尺寸,在人工神经网络、信号处理和模式识别等方面展现了巨大的应用前景。采用了基于STDP学习规则的忆阻神经网络,运用了网络自适应突变以及网络拓扑结构变化的基因算法,其中包括隐藏层神经元个数,连接权重以及神经网络突触模型的变化。比较了基于HP线性忆阻器模型,非线性忆阻器模型以及阈值模型这3种不同忆阻器模型的忆阻神经网络,并提出了学习效果更好的混合型忆阻神经网络。更多还原

【Abstract】 Due to the useful properties of nonvolatile,memory and nanoscale,memristors have prospective promising applications in artificial networks,pattern recognition and signal processing.This paper exploits the learning rule of the memristive network based on spiking timing dependent plasticity(STDP)and uses the genetic algorithms with self-adaptation and variable topologies,which allows the number of hidden neurons,connection weights,and connectivity pattern to change self-adaptably.Three memristor models are respectively used as the synapse in the network,including HP linear memristor,non-linear memristor and threshold memristor.The comparison of the performance of the three memristive neural networks is presented,and the hybrid memristive networks’ learning effects are analyzed.更多还原