An intelligent control method based on artifi cial neural network model
Abstract
The topology structure of the artifi cial neural network is an intelligent control model, which is used for intelligent vehicle
control system and household sweeping robot. When setting the intelligent control system, the connection point of each network is regarded
as a neuron in the nervous system, and each connection point has input and output functions. Only when the input of nodes reaches a
certain threshold can the output function of nodes be stimulated. Using the networking mode of artifi cial neural network model, the mobile
node can output in multiple directions. If the input direction of a certain path is the same as that of other nodes, it can choose to avoid and
choose another path. The weighted value of each path between nodes is diff erent, which means that the infl uence of the front node on the
current node is diff erent. The control method based on artifi cial neural network model can be applied to vehicle control, household sweeping
machine and other fi elds, and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.
control system and household sweeping robot. When setting the intelligent control system, the connection point of each network is regarded
as a neuron in the nervous system, and each connection point has input and output functions. Only when the input of nodes reaches a
certain threshold can the output function of nodes be stimulated. Using the networking mode of artifi cial neural network model, the mobile
node can output in multiple directions. If the input direction of a certain path is the same as that of other nodes, it can choose to avoid and
choose another path. The weighted value of each path between nodes is diff erent, which means that the infl uence of the front node on the
current node is diff erent. The control method based on artifi cial neural network model can be applied to vehicle control, household sweeping
machine and other fi elds, and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.
Keywords
Artifi cial neural network; The model; Control method; Optimization scheme.
Full Text:
PDFReferences
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DOI: https://doi.org/10.18686/esta.v10i4.628
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Copyright (c) 2023 LiangKai Zhou,DanHan,QinZheWang, HaiBoWang
