Abstract
Multiconstraint prediction is a research hotspot and difficulty in the Internet of Things. Aiming at the main problems existing in the process of multiconstraint fuzzy prediction, this paper studies the related key technologies and methods and proposes an improved multiconstraint fuzzy prediction analysis algorithm in the application of Internet of Things. This paper introduces the multiconstraint attribute analysis, multiconstraint attribute normalization processing, multiconstraint attribute weight processing, multiconstraint attribute prediction analysis granularity setting, etc. and systematically calculates the ambiguity of multiconstraint attributes and realizes the multiconstraint fuzzy prediction analysis algorithm. Finally, combined with relevant cases, the algorithm is compared with the existing research results, which shows the effectiveness and feasibility of the algorithm in the Internet of Things.
Highlights
The Internet of Things (IOT) refers to the real-time collection of any object or process that needs monitoring, connection and interaction, and the collection of various required information such as sound, light, heat, electricity, mechanics, chemistry, biology, and location through various devices and technologies such as information sensors, radio frequency identification technology, global positioning system, infrared sensors, and laser scanners
The Internet of Things is an information carrier based on the Internet and traditional telecommunication network
Assuming that the extracted multiconstraint attributes are marked with the number Q, the multiconstraint fuzzy prediction analysis set S is expressed as follows: È
Summary
The Internet of Things (IOT) refers to the real-time collection of any object or process that needs monitoring, connection and interaction, and the collection of various required information such as sound, light, heat, electricity, mechanics, chemistry, biology, and location through various devices and technologies such as information sensors, radio frequency identification technology, global positioning system, infrared sensors, and laser scanners. (2) For many practical constrained optimization problems, on the one hand, because the objective function is often complex, it is the problem with high dimension and the existence of several small points in the optimization surface, which makes the traditional gradient-based algorithm difficult to work. The research of fuzzy system prediction has analyzed some existing research results, especially literature [5, 6], based on the Grey Theory and neural network [7,8,9], vector machine, extension theory of genetic algorithm [10,11,12], etc., which has good engineering application effect. Based on the fuzzy system theory [13, 14], this paper analyzes the multiconstraint fuzzy prediction problem and proposes an improved multiconstraint fuzzy prediction analysis algorithm and model [15,16,17,18,19]
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