Abstract
Chaos is a common physical phenomenon in electrical control systems. The chaotic movement has high flexibility and sensitivity to the initial value, ergonomics and randomness of the movement trajectory. When maximizing and optimizing the design of the system, we can consider making full use of the chaotic ergonomics as the restraint of system maximization and optimization, to prevent the entire system from being trapped into a local minimum when it falls into search. The main reason for chaos optimization is to use chaotic variables to search for data within a certain range according to its availability and regularity, so that the search for chaotic variables exceeds the local minimum, and finally achieves the global maximum excellent. System optimization based on chaos theory is developing into a new global optimization method. In this paper, the chaotic algorithm of electrical control system based on neural network technology is researched. On the basis of related data, the chaotic phenomenon is generally understood, and then the characteristics of chaos are summarized on the basis, and then the chaos of electrical control system is explained. The discrimination method has laid the groundwork for the following experiments. Finally, the chaos algorithm of the electrical control system based on neural network technology is optimized. According to the experimental results, the optimized algorithm is nearly 10 s faster than the original algorithm. It can be seen that the optimized algorithm has better performance than the unoptimized algorithm.
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Zhu, Z. (2022). Chaos Algorithm of Electrical Control System Based on Neural Network Technology. In: Macintyre, J., Zhao, J., Ma, X. (eds) The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIoT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 98 . Springer, Cham. https://doi.org/10.1007/978-3-030-89511-2_9
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DOI: https://doi.org/10.1007/978-3-030-89511-2_9
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