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

Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Design Example: Capacitance Multiplier Circuit01:20

Design Example: Capacitance Multiplier Circuit

In integrated circuit technology, a capacitance multiplier is often utilized to produce a larger capacitance value when a small physical capacitance falls short. This is achieved by a circuit that multiplies capacitance values by a factor of up to 1000, such that a 10-pF capacitor can replicate the performance of a 100-nF capacitor.
The circuit illustrated in Figure 1 below incorporates two op-amps, with the first operating as a voltage follower and the second acting as an inverting amplifier.
MOS Capacitor01:25

MOS Capacitor

A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
Mesh Analysis for AC Circuits01:12

Mesh Analysis for AC Circuits

In the domain of radio communication, the significance of impedance matching must be considered. It is crucial to ensure the efficient transmission of signals between radio transmitters and receivers. Achieving this balance involves using impedance-matching circuits, with one fundamental configuration comprising a resistor, capacitor, and inductor.
The process of harmonizing these impedances begins with a clear understanding of the input and output signals. Once these signals are known, the...
Capacitor in an AC Circuit01:23

Capacitor in an AC Circuit

A capacitor is charged by passing an electric current through it, which causes the plates to start accumulating an electrostatic charge. Since the strength of the charging current is maximum when the capacitor plates are uncharged and gradually decreases exponentially until the capacitor is fully charged, the charging process is neither instantaneous nor linear. The property of a capacitor to store a charge on its plates is called its capacitance.
Consider a purely capacitive circuit consisting...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

You might also read

Related Articles

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

Sort by
Same author

CTAB-modified chitosan nanocoatings: Increasing the cationic surface character by self-assembly.

International journal of biological macromolecules·2026
Same author

Metal-Organic Framework Multizyme Colloids with Joint Antioxidant and Protease Function.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

[Long-COVID syndrome and lung-specific abnormalities following COVID-19].

Orvosi hetilap·2026
Same author

[The role of obesity and weight reduction in obstructive sleep apnea].

Orvosi hetilap·2026
Same author

Integrated Electrochemical Aptasensor-Polymer Inclusion Membrane Platform for Detecting Oxytetracycline in Raw Milk.

ACS sensors·2026
Same author

Evaluation of the diagnostic value of the Modified Evan's Blue Dye Test for assessing aspiration in tracheostomized critically ill patients: A systematic review and meta-analysis.

PloS one·2026

Related Experiment Videos

Kernel CMAC with improved capability.

Gábor Horváth1, Tamás Szabó

  • 1Department of Measurement and Information Systems, Budapest University of Technology and Economics, Hungary. horvath@mit.bme.hu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2007
PubMed
Summary

This study enhances the cerebellar model articulation controller (CMAC) by introducing a novel interpolation model and a regularized training algorithm. These advancements significantly improve CMAC's modeling and generalization capabilities for better approximation.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • The cerebellar model articulation controller (CMAC) offers fast learning and hardware efficiency.
  • However, open questions remain regarding its modeling and generalization capabilities.
  • Previous research has addressed modeling limits and some generalization properties.

Purpose of the Study:

  • To investigate and improve the modeling and generalization properties of CMAC.
  • To introduce a new interpolation model for CMAC.
  • To analyze and reduce the generalization error of CMAC.

Main Methods:

  • Introduction of a novel interpolation model for CMAC.
  • Detailed analysis of generalization error, including analytical expressions for special cases.
  • Development of a simple regularized training algorithm to minimize generalization error.
  • Discussion of differences between 1-D and multi-dimensional CMAC and introduction of a kernel-based interpretation.

Main Results:

  • Generalization error can be significant; a regularized training algorithm effectively reduces this error.
  • Differences in modeling capability exist between 1-D and multi-dimensional CMAC.
  • A kernel-based interpretation provides a unified framework, enabling similar modeling capabilities for both versions.
  • The regularized training algorithm, applied to kernel interpretations, significantly enhances approximation capabilities.

Conclusions:

  • The proposed interpolation model and regularized training algorithm improve CMAC's performance.
  • The kernel-based interpretation unifies CMAC versions and enhances modeling capabilities.
  • The regularized training algorithm is effective in improving CMAC's approximation capabilities, especially within the kernel framework.