基于权重的云推理算法

Weight-based cloud reasoning algorithm

虽然正态云模型具有普适性,但它在描述论域内单调上升或下降的概念时存在一些局限性,同时由于现有的云推理算法存在多条件下人为主观因素影响大、运算量大等问题,为此提出一种新的指数云模型来描述单调概念,并基于此提出一种基于权重的云推理算法。该算法将多条件发生器拆分为多个一维发生器,先通过层次分析法确定各个条件的属性权重,再采取加权平均法将单条件单规则发生器输出的结果精确化为一个具体的输出值。将基于权重的云推理算法用于鱼雷规避仿真系统中,并与模糊推理结果进行比较,验证了该算法的有效性和实用性。

Although the normal cloud model is universally used, it faces some difficulties when describing some monotonic rise/fall conceptions. This model also has big subjective influence under multiple conditions and large computation consumption. To overcome these shortcomings, a new kind of exponential cloud model was provided along with a weight based cloud reasoning algorithm. By splitting the multi-condition generator to several single-condition generators, the algorithm firstly used Analytic Hierarchy Process （AHP） method to get weight of each property, and then used them to calculate weighted average of single-condition generator output to quantiffy value. The validation and effectiveness of this method is checked through a comparison between fuzzy reasoning and stimulation of torpedo avoid system.