Optimization of energy consumption based on genetic algorithms optimization and fuzzy classification

At present, the error control method for high-speed serial data transmission obtains the errors by comparison and then controls them. If the data transmission channel is not denoised, the packet loss and error codes become serious, and energy consumption increases. The use of fuzzy classification is proposed to control data transmission errors. The method uses the combination of wavelet transform and transform domain difference to double denoise the channel, and it completes the clustering of data transmission errors by fuzzy classification. Considering packet loss, error codes, and energy consumption in data transmission error control, when the communication distance between two nodes is small, automatic repeat request is used to control data transmission errors. As the distance between nodes increases, forward error correction is used to control data transmission errors. When the communication distance gradually increases, data transmission errors are controlled by hybrid automatic repeat request. Experiments showed that the proposed method can reduce the data transmission error, control energy consumption, packet loss rate, and bit error rate, and enhance the denoising effect.

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Predicting China's Energy Consumption Using Artificial Neural Networks and Genetic Algorithms

2009 International Conference on Business Intelligence and Financial Engineering ◽

10.1109/bife.2009.11 ◽

2009 ◽

Author(s):

Shouchun Wang ◽

Xiucheng Dong

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Artificial Neural Networks ◽

Energy Consumption ◽

Artificial Neural

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Intelligent centralized traffic management of a rapid transit system under heavy traffic

Dependability ◽

10.21683/1729-2646-2021-21-2-17-23 ◽

2021 ◽

Vol 21 (2) ◽

pp. 17-23

Author(s):

L. A. Baranov ◽

V. G. Sidorenko ◽

E. P. Balakina ◽

L. N. Loginova

Keyword(s):

Genetic Algorithms ◽

Energy Consumption ◽

Control Theory ◽

Traffic Safety ◽

Energy Efficient ◽

Traffic Management ◽

Design Principles ◽

Rapid Transit ◽

Traffic Planning ◽

Transit System

Aim. In today’s major cities, increased utilization and capacity of the rapid transit systems (metro, light rail, commuter trains with stops within the city limits) – under condi[1]tions of positive traffic safety – is achieved through smart automatic train traffic management. The aim of this paper is to choose and substantiate the design principles and architecture of such system.Methods. Using systems analysis, the design principles and architecture of the system are substantiated. Genetic algorithms allow automating train traffic planning. Methods of the optimal control theory allow managing energy-efficient train movement patterns along open lines, assigning individual station-to-station running times following the principle of mini[1]mal energy consumption, developing energy-efficient target traffic schedules. Methods of the automatic control theory are used for selecting and substantiating the train traffic algorithms at various functional levels, for constructing random disturbance extrapolators that minimize the number of train stops between stations.Results. Development and substantiation of the design principles and architecture of a centralized intelligent hierarchical system for automatic rapid transit traffic management. The distribution of functions between the hierarchy levels is described, the set of subsystems is shown that implement the purpose of management, i.e., ensuring traffic safety and comfort of passengers. The criteria are defined and substantiated of management quality under compensated and non-compensated disturbances. Traffic management and target scheduling automation algorithms are examined. The application of decision algorithms is demonstrated in the context of uncertainty, use of disturbance prediction and genetic algorithms for the purpose of train traffic planning automation. The design principles of the algorithms of traffic planning and management are shown that ensure reduced traction energy consumption. The efficiency of centralized intelligent rapid transit management system is demonstrated; the fundamental role of the system in the digitalization of the transport system is noted.Conclusion. The examined design principles and operating algorithms of a centralized intelligent rapid transit management system showed the efficiency of such systems that ensured by the following: increased capacity of the rapid transit system; improved energy efficiency of train traffic planning and management; improved train traffic safety; assurance of operational traffic management during emergencies and major traffic disruptions; improved passenger comfort.

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