Evolutionary algorithm for sensor position optimization during space-time modeling of distributed parameter system based on two codes

A technology of distributed parameters and evolutionary algorithms, applied in genetic models, genetic laws, design optimization/simulation, etc., can solve problems such as a large number of feasible solutions and time-consuming

Pending Publication Date: 2021-05-28
CENT SOUTH UNIV
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Problems solved by technology

The number of feasible solutions to the sensor optimization problem is huge. At this time, the traditional evolutionary algorithm is generally too time-consuming to solve the problem.

Method used

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  • Evolutionary algorithm for sensor position optimization during space-time modeling of distributed parameter system based on two codes
  • Evolutionary algorithm for sensor position optimization during space-time modeling of distributed parameter system based on two codes
  • Evolutionary algorithm for sensor position optimization during space-time modeling of distributed parameter system based on two codes

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Embodiment Construction

[0013] A kind of sensor position optimization evolution method with two kinds of encoding mechanisms designed by the present invention with minimum spatio-temporal modeling error is as follows:

[0014] Step 1: Set the parameters of the algorithm, the maximum number of iterations genMax, the number of global search iterations Nt, and the number of local search iterations Ns.

[0015] Step 2: Using the first encoding method, an individual conducts a global search for a sensor layout: search for a set of sensor placement locations in the current entire distributed parameter system space, randomly select several groups of locations as an initial sensor layout population, and judge each group Whether the sensor layout position is feasible, it is feasible to adjust the position of the infeasible sensor. Then construct the error size and differential evolution algorithm calculated by using the initial population layout, and generate a new layout group called offspring. If the offspr...

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Abstract

The invention discloses an evolutionary algorithm for sensor position optimization during space-time modeling of a distributed parameter system based on two codes, and belongs to the field of intelligent systems and mode recognition. The sensor layout scheme is effectively combined and optimized by mixing two coding mechanisms provided by the invention, and the optimal sensor layout scheme can be determined to minimize the space-time modeling error of the distribution parameter system. The invention comprises the following steps: initializing the initial position layout of each sensor, selecting the temperature data of the position according to the layout of the sensor to carry out distribution system space-time modeling, namely decomposing a distribution parameter system into a time function and a space function according to KLD, carrying out rbf neural network fitting on the time function, and finally estimating the error of a combined model, performing global search and local search by using two coding modes and a differential evolution algorithm, continuously adjusting the positions of the sensors to obtain different calculation errors, and finally placing the sensors with a limited number according to the layout with the minimum error. The space-time modeling error is minimized, the stable operation of the distributed parameter system is maintained, and the robustness of predicting and monitoring the distributed parameter system is improved.

Description

technical field [0001] The invention relates to a sensor position optimization method for time-space modeling of a distributed parameter system based on two types of codes, which can be applied to sensor layout position optimization and belongs to the field of intelligent systems and pattern recognition. Background technique [0002] Advanced technological demands in semiconductor manufacturing, nanotechnology, biotechnology, materials engineering, and chemical engineering drive the control of material microstructure, fluid flow, spatial distribution (e.g., temperature field), and product size distribution. Whereas physical, chemical or biological processes lead to systems with distributed parameters, where inputs, outputs and even parameters may vary in time and space. Typical examples include thermal processes, fluid processes, convection-diffusion-reaction processes. With the development of sensors, actuators, and computing technologies, the study of distributed parametr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/18G06F30/27G06N3/04G06N3/08G06N3/12G06F119/08G06F119/12
CPCG06N3/049G06N3/086G06N3/126G06F30/18G06F30/27G06F2119/08G06F2119/12
Inventor 何诗慧王勇
Owner CENT SOUTH UNIV
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