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

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Integration by Parts: Problem Solving01:29

Integration by Parts: Problem Solving

Smart speakers process voice commands by modeling audio inputs as piecewise functions and analyzing them through integration against trigonometric functions, such as cosine. This mathematical approach is fundamental in signal processing, where complex sound waves are decomposed into simpler frequency components.Consider a definite integral involving a piecewise function multiplied by a cosine function. Because the function is defined differently over separate intervals, the integral is split...
Piecewise-Defined Functions01:28

Piecewise-Defined Functions

Piecewise defined functions are mathematical models where different expressions define a function over distinct intervals of the domain. These functions are useful for representing systems with varying behaviors depending on input values.For example, the function:  uses a linear rule for inputs less than or equal to –1 and a quadratic rule for values greater than –1. Although it has two formulas, it still defines a single function.Another common type is the absolute value function, given...
Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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 of...
Perception01:28

Perception

Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...

You might also read

Related Articles

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

Sort by
Same author

Anatomical and molecular characterization of some rhigonematid parasites of millipedes in Nigeria, with new insights into their phylogeny.

Journal of helminthology·2023
Same author

Association of Hashimoto's thyroiditis with thyroid cancer.

Endocrine-related cancer·2014
Same author

Generating sensor data summaries to communicate change in elders' health status.

Applied clinical informatics·2014
Same author

The influence of contemporary and historic landscape features on the genetic structure of the sand dune endemic, Cirsium pitcheri (Asteraceae).

Heredity·2014
Same author

Parathyroid hormone-related protein inhibits DKK1 expression through c-Jun-mediated inhibition of β-catenin activation of the DKK1 promoter in prostate cancer.

Oncogene·2013
Same author

Characteristics of natural scenes related to the fractal dimension.

IEEE transactions on pattern analysis and machine intelligence·2011

Related Experiment Videos

Incorporating fuzzy membership functions into the perceptron algorithm.

J M Keller1, D J Hunt

  • 1Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65201.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

The fuzzy perceptron algorithm improves pattern recognition by using fuzzy set theory to handle non-separable data, enhancing convergence where the standard perceptron fails. This fuzzy approach ensures reliable decision boundary determination even with complex datasets.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • The perceptron algorithm, a gradient descent technique, is standard for linear decision boundaries in pattern recognition.
  • It reliably converges for linearly separable data but struggles with non-separable datasets.

Purpose of the Study:

  • To introduce fuzzy set theory into the perceptron algorithm to address convergence issues with non-separable data.
  • To develop a "fuzzy perceptron" that enhances stability and reliability in pattern recognition tasks.

Main Methods:

  • Integration of fuzzy set theory principles into the standard perceptron algorithm.
  • Development of a novel method for generating membership functions tailored for the fuzzy perceptron.
  • Comparative experimental analysis of the fuzzy perceptron against the traditional "crisp" perceptron.

Main Results:

  • The fuzzy perceptron demonstrates convergence in linearly separable cases, similar to the crisp perceptron.
  • The fuzzy perceptron effectively ameliorates the erratic convergence behavior observed in the non-separable case.
  • Experimental results validate the improved performance and robustness of the fuzzy perceptron.

Conclusions:

  • Fuzzy set theory offers a robust enhancement to the perceptron algorithm for pattern recognition.
  • The fuzzy perceptron provides a more reliable solution for datasets that are not linearly separable.
  • This modified algorithm expands the applicability of perceptron-based methods in machine learning.