基于阈值自适应忆阻器Hopfield神经网络的关联规则挖掘算法

针对基于Hopfield神经网络的最大频繁项集挖掘（HNNMFI）算法存在的挖掘结果不准确的问题，提出基于电流阈值自适应忆阻器（TEAM）模型的Hopfield神经网络的改进关联规则挖掘算法。首先，使用TEAM模型设计实现突触，利用阈值忆阻器的忆阻值随方波电压连续变化的能力来设定和更新突触权值，自适应关联规则挖掘算法的输入。其次，改进原算法的能量函数以对齐标准能量函数，并用忆阻值表示权值，放大权值和偏置。最后，设计由最大频繁项集生成关联规则的算法。使用10组大小在30以内的随机事务集进行1000次仿真实验，实验结果表明，与HNNMFI算法相比，所提算法在关联挖掘结果准确率上提高33.9个百分点以上，说明忆阻器能够有效提高Hopfield神经网络在关联规则挖掘中的结果准确率。

Association rule mining algorithm for Hopfield neural network based on threshold adaptive memristor

Aiming at the inaccurate mining results of the Maximum Frequent Itemset mining algorithm based on Hopfield Neural Network (HNNMFI), an improved association rule mining algorithm for Hopfield neural network based on current ThrEshold Adaptive Memristor (TEAM) model was proposed. Firstly, TEAM model was used to design and implement synapses whose weights were set and updated by the ability of that threshold memristor continuously changes memristance value with square-wave voltage, and the input of association rule mining algorithm was self-adapted by the neural network. Secondly, the energy function was improved to align with standard energy function, and the memristance values were used to represent the weights, then the weights and bias were amplified. Finally, an algorithm of generating association rules from the maximum frequent itemsets was designed. A total of 1000 simulation experiments using 10 random transaction sets with size less than 30 were performed. Experimental results show that compared with HNNMFI algorithm, the proposed algorithm improves the result accuracy of association mining by more than 33.9%, which indicates that the memristor can effectively improve the result accuracy of Hopfield neural network in association rule mining.