A new cognitive model: Cloud model

Abstract

Randomness and fuzziness are the two most important uncertainties inherent in human cognition, which have attracted great attention in artificial intelligence research. In this paper, regarding linguistic terms or concepts as the basic units of human cognition, we propose a new cognitive model-cloud model, which can synthetically describe the randomness and fuzziness of concepts and implement the uncertain transformation between a qualitative concept and its quantitative instantiations. Furthermore, by analyzing in detail the statistical properties of normal cloud model, that is, an important kind of cloud models based on normal distribution and Gauss membership function, we show that normal cloud model can not only be viewed as a generalized normal distribution with weak constraints but also avoid the flaw of fuzzy sets to quantify the membership degree of an element as an accurate value between 0 and 1 and, therefore, may be more adaptive for the uncertainty description of linguistic concepts. Finally, two demonstration examples about the fractal evolution of plants and network topologies based on cloud models are given to illustrate the promising applications of cloud models in some more complex knowledge representation tasks. C 2009 Wiley Periodicals, Inc.

Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method

“…The three parameters E x ,E n ,H e and the number of cloud drops n are given to compare the cloud theoretically by forward normal cloud generator as described in [55]. Then the expected valueŝ of cloud A is denoted as follows:ŝ pAq " p1{nq ÿ n i"1…”

“…On the one hand, the cloud model was first described by Li [55], an expert in command automation and artificial intelligence from the Chinese Academy of Engineering. The cloud model comprehensively analyzes the randomness and fuzziness of the uncertainty, which can greatly reduce the loss of useful information.…”

Granular computing: from granularity optimization to multi-granularity joint problem solving

“…A qualitative concept C is expressed by numerical characteristics (Ex, En, He), wherein, Ex is the core of a concept, En denotes the granularity scale of the concept, and He denotes the uncertainty of the concept's granularity. It better characterizes the bidirectional cognitive transformation process of intension and extension of a concept, revealing the randomness and fuzziness of objective entity (Li et al 2009), as shown in Fig. 2.…”

Optimal Site Selection of Wind-Solar Complementary Power Generation Project for a Large-Scale Plug-In Charging Station

“…The cloud model was first proposed by Academician Li D. [37] of the Chinese Academy of Engineering for comprehensively analyzing the randomness and fuzziness in uncertain language and maximally reducing the effective information loss in uncertain language, such as the site selection of offshore wind power, refuse landfill and wind power plant. Here, in the PROMETHEE method is expanded to uncertain language environment using the cloud model; a model employing the AHP method for solving index weight is applicable to the site selection optimization research of wind-solar hybrid power generation project interconnected to large charge/battery-replacement stations and is favorable for decision implementation; further it can ensure the scientific validity and rationality of the decision result.…”