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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.
In the absence...
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Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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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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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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  2. 主导分类器辅助的混合进化多目标神经架构搜索
  1. 首页
  2. 主导分类器辅助的混合进化多目标神经架构搜索

相关实验视频

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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主导分类器辅助的混合进化多目标神经架构搜索

Yu Xue1, Keyu Liu1, Ferrante Neri1,2

  • 1School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China.

International journal of neural systems
|July 31, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

CHENAS加速神经架构搜索 (NAS) 实现多目标深度学习,使用混合进化方法. 它采用了新的分类器和自动编码器,以提高预测准确性和神经网络设计的效率.

关键词:
神经架构搜索神经架构搜索进化算法是一种进化算法.多目标优化多目标优化通过代理母亲的协助.

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 神经架构搜索 (NAS) 自动化了深度神经网络设计,但在计算上是密集的,特别是在多目标问题上.
  • 目前的预测器辅助进化NAS方法面临着缓慢的融合和排名障碍的挑战,影响预测的准确性.
  • 这些局限性阻碍了对最佳神经网络架构的有效发现.

研究的目的:

  • 引入CHENAS,一个由分类器辅助的多目标混合进化NAS框架.
  • 在多目标NAS中提高融合速度和解决方案质量.
  • 在现有的NAS方法中解决缓慢的融合和排名障碍的问题.

主要方法:

  • CHENAS集成了用于全球勘探的进化算法和基于梯度的优化,用于本地改进.
  • 一个新的统治性分类器将多目标优化重新定义为分类任务,以预测帕雷托统治关系.
  • 一个基于对比学习的自动编码器创建了一个结构化的潜在空间,以改善主导预测.

主要成果:

  • 在基准数据集上,CHENAS与最先进的NAS方法相比表现优越.
  • 该框架有效地识别了跨多个目标的高性能架构.
  • CHENAS减轻了排名障碍,并提高了多目标NAS中的预测准确性.

结论:

  • CHENAS为多目标神经架构搜索提供了一个高效和有效的框架.
  • 提出的分类器和自动编码器显著提高了预测准确性和趋同性.
  • 未来的研究将集中在计算效率和更广泛的应用领域.