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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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卷积神经网络-多核辐射基函数神经网络-鱼群算法:用于预测废水质量参数的新机器学习模型.

Zohreh Sheikh Khozani1, Mohammad Ehteram2, Wan Hanna Melini Wan Mohtar3

  • 1Paleoclimate Dynamics Group, Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, 27570, Bremerhaven, Germany.

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概括

一个新的深度学习模型准确地预测了废水废水质量参数. 这种先进的系统,将CNN和MKRBFNN与SSA优化相结合,提供了强大的废水处理厂监控.

关键词:
人工神经网络的人工神经网络深度学习是一种深度学习.优化算法的优化算法废水处理厂的废水处理厂.

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科学领域:

  • 环境工程 环境工程
  • 人工智能的人工智能
  • 水资源管理 水资源管理

背景情况:

  • 污水处理厂 (WWTP) 对城市水循环和减少污染至关重要.
  • 为了有效地监测和控制WWTP,需要先进的建模技术.
  • 现有的模型往往缺乏同时预测和不确定性估计的能力.

研究的目的:

  • 引入一种新的深度学习模型,用于预测废水废水质量参数 (EQP).
  • 开发一种混合模型,将卷积神经网络 (CNN) 与多核辐射基函数神经网络 (MKRBFNN) 结合起来.
  • 集成Salp Swarm算法 (SSA) 来优化模型参数和增强特征提取.

主要方法:

  • 一个混合CNN-MKRBFNN模型被开发用于预测EQPs.
  • 使用Salp Swarm算法 (SSA) 来优化CNN和MKRBFNN参数.
  • 影响参数 (BOD,COD,TSS,VSS) 被用作预测废水参数 (COD,BOD,TSS) 的输入.

主要成果:

  • CNN-MKRBFNN-SSA模型实现了高的纳什-萨特克利夫效率:COD为0.98,BOD为0.97和TSS为0.98.
  • 该模型在模拟复杂的废水处理现象方面表现出强大的性能.
  • 成功实现了环境素质计划的同时预测和不确定性估计.

结论:

  • 拟议的CNN-MKRBFNN-SSA模型是WWTP废水质量预测的强大工具.
  • 混合深度学习方法在准确性和稳定性方面提供了显著的改进.
  • 这种方法提高了废水处理过程的监测和控制能力.