BRINK
发表于 2025-3-30 10:16:07
https://doi.org/10.1007/978-3-663-08997-1e. Simulations and actual test results show that the PID controller based on BP neural network has strong adaptability and robustness in a nonlinear system. It can both save energy and solve the problem of drastically fluctuating illumination in a moving metro, which cannot be achieved in conventional PID controllers.
Epithelium
发表于 2025-3-30 13:21:16
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Esophagus
发表于 2025-3-30 16:45:56
Grundlagen des Landfill Mining,values were sent back to fuzzy logic controller. Simulation results concerning two-tank system show that the DE optimized fuzzy controller has good adaptability, as well as it‘s effectiveness, which provides a new approach to improve fuzzy control system.
凌辱
发表于 2025-3-30 23:47:43
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最高点
发表于 2025-3-31 02:47:20
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Vulvodynia
发表于 2025-3-31 05:37:37
Prozess der strategischen Steuerungoarse search and fine search, cooperative search and learning. Finally, the proposed MCPSO was applied to some well-known benchmarks. The experimental results and relative analysis show mixed swarm cooperative search mechanism can greatly benefit the global optimization performance of PSO.
hypnogram
发表于 2025-3-31 10:09:31
https://doi.org/10.1007/978-3-322-81740-2 developed DQSE outperforms the quantum-inspired evolutionary algorithm, the quantum-inspired evolutionary algorithm with NOT gate and QSE in terms of the search accuracy and the convergence speed, which demonstrates that DQSE is an effective and efficient optimization algorithm.
参考书目
发表于 2025-3-31 14:54:06
Kundenorientierung und Positionierung,rding to the CamShift algorithm features, in this article, I will combine Adaboost, CamShift and Kalman filtering algorithm, which can be relied on to realize face detection and tracking automatically and accurately.
Maximizer
发表于 2025-3-31 18:39:48
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Trochlea
发表于 2025-4-1 01:06:01
A Study of Adaptive Neural Network Control Systemhydraulic system, the response and stability are the key factors to judge the control method is good or not. In our simulation, it shows that this adaptive neural network control system can meet the design requirements. It has good response and stability characteristics.