Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
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...
149
Neural Regulation01:37

Neural Regulation

39.9K
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.
39.9K
Associative Learning01:27

Associative Learning

572
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
572
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

131
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
131
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

124
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
124

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Adaptive Beamforming Applied to OFDM Systems.

Sensors (Basel, Switzerland)·2018
See all related articles

Related Experiment Video

Updated: Sep 10, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.7K

Adaptive Learning Rate Methods for Complex-Valued Neural Networks.

Kayol S Mayer, Jonathan A Soares, Ariadne A Cruz

    IEEE Transactions on Neural Networks and Learning Systems
    |August 21, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces novel adaptive learning rate methods for complex-valued neural networks (CVNNs), enhancing their training efficiency and performance in digital signal processing applications.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.4K

    Related Experiment Videos

    Last Updated: Sep 10, 2025

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.7K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.4K

    Area of Science:

    • Digital Signal Processing (DSP)
    • Machine Learning
    • Complex-Valued Neural Networks (CVNNs)

    Background:

    • Artificial neural networks (ANNs) are widely used in DSP.
    • Complex-valued neural networks (CVNNs) offer advantages over real-valued neural networks (RVNNs) in handling complex domain signals, leading to higher accuracy and faster convergence.
    • However, CVNNs lack advanced learning techniques compared to RVNNs.

    Purpose of the Study:

    • To propose adaptive learning rate optimization approaches for CVNNs.
    • To extend established adaptive gradient algorithms to the complex domain for CVNNs.
    • To analyze the computational complexity and performance of these novel CVNN optimizers.

    Main Methods:

    • Extension of AdaGrad, RMSProp, AdaMax, AMSGrad, SAMSGrad, Nadam, and DiffGrad to the complex domain.
    • Analysis of computational complexities for CVNN architectures using the proposed optimizers.
    • Comparative evaluation of mean-squared-error convergence for different adaptive learning rate approaches.

    Main Results:

    • The proposed adaptive learning rate methods are successfully extended to the complex domain for CVNNs.
    • Computational complexities of the novel optimizers are analyzed for CVNNs.
    • Performance is evaluated based on mean-squared-error convergence, demonstrating potential improvements.

    Conclusions:

    • The developed adaptive learning rate approaches enhance the training of CVNNs.
    • These methods address the gap in learning techniques for CVNNs, potentially improving their applicability in image processing and telecommunications.
    • Further research can explore broader applications and optimizations of these complex-valued adaptive learning algorithms.