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Modeling of Diode Reverse Characteristics01:14

Modeling of Diode Reverse Characteristics

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In electronic circuits, reverse-biased diode configurations are critical for regulating voltage levels. Zener diodes exploit the reverse breakdown phenomenon and exhibit a controlled breakdown at a specific Zener voltage (VZ). They are designed to maintain a constant voltage across their terminals and are commonly used for voltage regulation in circuits.
When a reverse voltage applied to a Zener diode exceeds its breakdown voltage, the diode enters the breakdown region. At this point, the...
443
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

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The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
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Neural Circuits01:25

Neural Circuits

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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.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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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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Modeling of Diode Forward Characteristics01:19

Modeling of Diode Forward Characteristics

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Understanding the behavior of diodes when forward-biased is a fundamental aspect of electronic circuit design and analysis. This analysis primarily utilizes two models: the exponential diode model and the constant-voltage-drop model. The exponential model comes into play when the source voltage exceeds 0.5 volts, pushing the diode current to rise exponentially above the saturation current. This relationship is graphically depicted in the current-voltage (I-V) curve, illustrating the diode's...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

217
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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Related Experiment Video

Updated: Nov 1, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.9K

Diagonal Recurrent Neural Network-Based Hysteresis Modeling.

Guangzeng Chen, Guangke Chen, Yunjiang Lou

    IEEE Transactions on Neural Networks and Learning Systems
    |June 18, 2021
    PubMed
    Summary
    This summary is machine-generated.

    The Preisach model is a diagonal recurrent neural network (dRNN). This study reveals dRNNs can model rate-dependent hysteresis and approximate the Preisach model, outperforming it in accuracy and efficiency.

    Related Experiment Videos

    Last Updated: Nov 1, 2025

    Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
    10:50

    Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

    Published on: June 21, 2022

    1.9K

    Area of Science:

    • * Computational neuroscience
    • * Machine learning
    • * Mathematical modeling

    Background:

    • * Hysteresis modeling is crucial in various scientific and engineering fields.
    • * Preisach models and neural networks are leading strategies for hysteresis modeling.
    • * Existing neural network approaches often treat hysteresis as a black box.

    Purpose of the Study:

    • * To mathematically establish the equivalence between the Preisach model and a specific type of neural network.
    • * To investigate the hysteresis characteristics of diagonal recurrent neural networks (dRNNs) with the tanh activation function.
    • * To demonstrate the capability of dRNNs in modeling both rate-dependent and rate-independent hysteresis.

    Main Methods:

    • * Mathematical proof establishing the Preisach model as a dRNN with a binary step activation function.
    • * Theoretical analysis of the hysteresis nature and conditions of dRNNs with tanh activation.
    • * Experimental validation comparing dRNN performance against the Preisach model.

    Main Results:

    • * The rate-independent Preisach model is mathematically proven to be a dRNN with a binary step activation function.
    • * dRNNs with tanh activation exhibit rate-dependent hysteresis under specific conditions.
    • * dRNNs can approximate the Preisach model with high precision for rate-independent hysteresis.
    • * Experiments demonstrate superior accuracy and efficiency of dRNNs over the Preisach model for both hysteresis types.

    Conclusions:

    • * dRNNs offer a unified framework for understanding and modeling hysteresis.
    • * The mathematical insights into dRNNs enable more effective hysteresis modeling.
    • * dRNNs present a more accurate and efficient alternative to the Preisach model for hysteresis applications.