中文摘要：

提出了一种对线性不可分数据集进行分类的电流模式线性分类器．该分类器电路结构简单，仅由梯形激活函数电路和线性加权电路组成，其中线性加权电路采用全平衡差分跨导电路实现，梯形激活函数电路主要由阈值电路组成．为了实现对线性不可分数据集的分类，通过MATLAB软件采用Fisher线性判别法计算得到权重系数，并运用PSPICE对所提出的电路进行仿真分析．结果表明：提出的电路结构简单、准确度高、功耗低，可以广泛地应用于模式识别、神经网络、人工智能等领域．

英文摘要：

In this paper, a current - mode linearly classifier which can classify linearly non - separable data is proposed. The proposed classifier circuit is composed of trapezoidal activation function circuits and linearly weighted circuits. Linearly weighted circuit is achieved by fully balanced differential transconductor and trape- zoidal activation function circuit is mainly comprised of threshold circuits. In order to achieve the classification of the linearly non - separable dataset, the weighting coefficients can be obtained based on Fisher＇ s linear discrimi- nant analysis by MATLAB software. The proposed circuit is simulated and analyzed by PSPICE. The results show that the proposed circuit recognizes the characteristics of simple structures with high accuracy and low power con- sumption. The proposed classifier circuit has potential applications in various fields such as pattern recognition,neural networks and artificial intelligence.