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世界上许多概念都是模糊的,但人们却能根据这些概念进行推理,得出明确的结论。初看起来,模糊似乎不是一件好事,事实上却相反,它可用较少的代码传递足够的信息,并能对复杂事物做出高效率的判断和处理。模糊控制是用模糊逻辑实现精确控制的一项技术。模糊控制包括输入级、处理级和输出级3个过程。输入级将输入数据转变为模糊数据,称为模糊化;处理级用模糊数据及模糊逻辑规则推理求解;输出级将模糊数据变成精确数据输出以进行控制,称为逆模糊化。模糊控制的优点是,当过程的数学模型不存在或虽存在但编码十分困难时,可考虑采用模糊控制;采用不太昂贵的传感器和低性能的处理器,就能达到相当好的控制性能;可应用于环境噪声水平高的场合;能巧妙地综合直觉经验,在具有纯滞后、大惯量、参数漂移大的非线性不确定分布参数系统中,实现较满意的控制。缺点是,精度不高,容易产生振荡,适应能力有限。当前,模糊控制作为一种新型控制技术,正处于蓬勃发展时期,已开发和研制的控制器有3类:自调整型模糊控制器、混合型模糊控制器和自学习型模糊控制器。本期选登的4篇论文,一篇属自调整型,三篇为混合型。
Many of the world’s concepts are vague, but one can reason from these concepts and reach a clear conclusion. At first glance, it seems that blurring is not a good thing, but in fact the opposite is that it can deliver enough information with less code and can make efficient judgments and handling of complex things. Fuzzy control is a technique that uses fuzzy logic to achieve precise control. Fuzzy control includes input stage, processing stage and output stage 3 processes. The input stage converts the input data into fuzzy data, which is called fuzzification. The processing stage uses fuzzy data and fuzzy logic rules to reason and solve. The output stage converts the fuzzy data into precise data output for control, which is called inverse fuzzification. The advantage of fuzzy control is that fuzzy control can be considered when the mathematical model of the process does not exist or exists but coding is very difficult. The control performance is very good with less expensive sensors and low-performance processors. It can be used in situations where the ambient noise level is high. It can cleverly integrate intuitional experience to achieve more satisfactory control in a nonlinear uncertain distribution parameter system with pure hysteresis, large inertia and large parameter drift. The disadvantage is that the accuracy is not high, prone to oscillation, limited ability to adapt. At present, as a new control technology, fuzzy control is in a period of vigorous development. There are three types of controllers that have been developed and developed: self-tuning fuzzy controllers, hybrid fuzzy controllers and self-learning fuzzy controllers. Selected papers published in this issue, one is a self-adjusting type, three for the mixed type.