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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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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

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In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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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.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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双阶段合作多目标进化算法,由受约束敏感变量指导.

Jun Ma, Yong Zhang, Dun-Wei Gong

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    此摘要是机器生成的。

    本研究引入了一种新的进化算法 (CV-TCMOEA),通过专注于限制敏感变量来更好地处理工程优化问题. 这种新的方法提高了复杂问题的性能.

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    科学领域:

    • 工程优化工程优化
    • 计算智能是一种计算智能.
    • 进化计算是一种进化计算.

    背景情况:

    • 有限制的多目标优化问题 (CMOPs) 在工程中很普遍.
    • 现有的受约束多目标进化算法 (CMOEA) 往往无法在违反约束时区分变量的重要性.
    • 需要算法有效处理受约束敏感变量.

    研究的目的:

    • 提出一个新的双阶段合作多目标进化算法 (CV-TCMOEA),以限制敏感变量为指导.
    • 解决现有的CMOEA在处理具有不同程度约束影响的变量方面的局限性.
    • 根据可变灵敏度,开发一种适应性战略,以更新个体.

    主要方法:

    • 一个两阶段的方法,涉及一个辅助问题和一个合作的搜索.
    • 将决策变量分为受约束敏感类型和不受约束敏感类型.
    • 一个采用多策略的可变类型引导的合作个人更新策略.

    主要成果:

    • 拟议的CV-TCMOEA与七个最先进的CMOEA相比,表现优越.
    • 在28个基准函数和10个工程问题中验证了有效性.
    • 该算法成功地解决了受约束敏感变量的挑战.

    结论:

    • CV-TCMOEA提供了一个有效的机制来处理CMOP中的约束敏感变量.
    • 拟议的战略提高了工程应用进化算法的性能和稳定性.
    • 这项工作通过自适应变量处理来推进受约束的多目标优化领域.