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Related Concept Videos

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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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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Implicit Differentiation: Problem Solving01:29

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Curves defined implicitly, where variables cannot be separated algebraically, require specialized techniques for analysis. The conchoid of Nicomedes exemplifies such a case. Its equation links x and y in a way that prevents isolation of one variable, making implicit differentiation essential to determine the slope and behavior at any point on the curve.The implicit form of the conchoid can be expressed as:To differentiate this equation, y is treated as a function of x, and the chain rule is...
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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.
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Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

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Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
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Decision Making: P-value Method01:09

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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Updated: Apr 12, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Model-Free Dual Heuristic Dynamic Programming.

Zhen Ni, Haibo He, Xiangnan Zhong

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2015
    PubMed
    Summary
    This summary is machine-generated.

    A new model-free dual heuristic dynamic programming (MF-DHP) approach reduces computational cost for control problems. This method achieves competitive performance compared to model-based DHP (MB-DHP) by using online training and finite-difference techniques.

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    Last Updated: Apr 12, 2026

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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    Area of Science:

    • Control Theory
    • Machine Learning
    • Dynamic Programming

    Background:

    • Model-based dual heuristic dynamic programming (MB-DHP) is widely used for optimal control approximations.
    • MB-DHP typically requires computationally expensive offline training for its model network.

    Purpose of the Study:

    • To propose a novel model-free dual heuristic dynamic programming (MF-DHP) approach.
    • To reduce the computational burden associated with traditional MB-DHP methods.

    Main Methods:

    • Developed an MF-DHP design utilizing a finite-difference technique.
    • Employed multilayer perceptrons with a single hidden layer for action and critic networks.
    • Implemented delayed objective functions for online training of both networks.

    Main Results:

    • MF-DHP demonstrated control performance comparable to MB-DHP in both discrete and continuous time examples.
    • The MF-DHP approach significantly reduced computational resource requirements compared to MB-DHP.

    Conclusions:

    • The proposed MF-DHP offers a computationally efficient alternative to MB-DHP for control problems.
    • MF-DHP achieves competitive performance without the need for offline model training, making it a practical solution.