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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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BatTS:一种用于优化深度前神经网络的混合方法.

Sichen Pan1, Tarun Kumar Gupta2, Khalid Raza2

  • 1School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, Guangdong Province, China.

PeerJ. Computer science
|June 22, 2023
PubMed
概括
此摘要是机器生成的。

设计深度前神经网络 (DFNN) 架构具有挑战性. 一种新的混合 BatTS 方法使用 Bat 算法和 Tabu 搜索优化了 DFNN 架构,提高了随机试验的性能.

关键词:
一个年龄,一个年龄.优化优化 优化优化塔布搜索 塔布搜索 塔布搜索

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 深度前神经网络 (DFNNs) 在计算任务中取得了显著的成功.
  • 目前的DFNN架构选择依赖于低效的手动或试错方法.
  • 自动化DFNN架构设计对于实现最先进的性能至关重要,但仍然是繁的.

研究的目的:

  • 引入一种新的混合方法,BATTS,用于优化深度前神经网络架构.
  • 为了提高DFNN架构设计流程的效率和有效性.
  • 通过自动化架构优化来提高DFNNs的整体性能.

主要方法:

  • 一种混合方法 (BatTS) 整合了蝙蝠算法,塔布搜索 (TS) 和渐变下降与动量 (GDM) 逆向传播.
  • 由蝙蝠算法指导的动态架构生成.
  • 高效的架构评估和局部最佳逃脱,由 Tabu 搜索提供便利.
  • 在四个不同的基准数据集上进行实证评估.

主要成果:

  • 与传统的 Tabu 搜索和随机试验方法相比,BatTS 显示出更好的性能.
  • 混合方法在探索和评估新架构方面表现出更高的效率.
  • BatTS的动态性质有助于发现优越的DFNN架构.
  • 在多个基准数据集中观察到一致的性能增长.

结论:

  • 拟议的BatTS混合方法为优化DFNN架构提供了更有效和高效的方法.
  • 通过将元启发式优化与强有力的训练相结合,BatTS克服了手动设计和随机搜索的局限性.
  • 这项工作为寻求通过智能架构设计推进DFNN性能的研究人员和从业人员提供了有价值的工具.