Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundan...Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.展开更多
基于前置放大器对于高稳定性基准电流源的需求,设计了一种结构简单、对温度不敏感、有较高电压稳定性的三支路基准源电路。利用与绝对温度成比例(Proportional to Absolute Temperature,PTAT)和与绝对温度互补变化(Complementary to Abs...基于前置放大器对于高稳定性基准电流源的需求,设计了一种结构简单、对温度不敏感、有较高电压稳定性的三支路基准源电路。利用与绝对温度成比例(Proportional to Absolute Temperature,PTAT)和与绝对温度互补变化(Complementary to Absolute Temperature,CTAT)的电流产生一个对温度不敏感的电流。同时,利用第三支路产生的电压增益形成的负反馈回路来提高电路的稳定性,减少电源电压波动对输出电流产生的影响。对电流源集成电路拓扑结构进行前仿和后仿,结果表明:当电源电压供电5 V时,在-25~145℃,温度系数较低;在0~15.8 kHz范围内,电源抑制比(Power Supply Rejection Ratio,PSRR)为-80 dB左右;整体静态电流仅为20μA。该拓扑结构在设计低温漂高电源抑制比方面具有非常好的效果,并且可以很好的为前置放大器提供稳定的工作电流。展开更多
文摘Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.
