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

Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Neural Circuits01:25

Neural Circuits

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...

You might also read

Related Articles

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

Sort by
Same author

Anti-Disturbance Intermediate Observer-Based Fault Estimation and Fault-Tolerant Control for Markovian Jump Systems.

IEEE transactions on cybernetics·2026
Same author

Intravenous dextrose for post-gastrointestinal endoscopy dizziness.

Gastrointestinal endoscopy·2026
Same author

A novel dynamic signal Lemma for predefined-time stabilization of high-order nonlinear systems with dynamic uncertainties.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Observer-Based Fuzzy Secure Control for High-Order MASs Against Communication Delays Under Jointly Connected Switching Topology.

IEEE transactions on cybernetics·2026
Same author

TSFA: A Two-Stage Feature Alignment Method for Unsupervised Open-Set Domain Adaptation in Time-Series Classification.

IEEE transactions on neural networks and learning systems·2026
Same author

How do core personality traits influence short video dependence among Chinese college students? Evidence from a serial mediation analysis under the I-PACE model.

Frontiers in psychology·2026
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

Related Experiment Videos

Data-core-based fuzzy min-max neural network for pattern classification.

Huaguang Zhang1, Jinhai Liu, Dazhong Ma

  • 1School of Information Science and Engineering, Northeastern University, Shenyang 110004, China. Zhanghuaguangieee@gmail.com

IEEE Transactions on Neural Networks
|December 8, 2011
PubMed
Summary
This summary is machine-generated.

A novel fuzzy min-max neural network (FMNN) using a data core (DCFMN) improves pattern classification. This DCFMN offers enhanced robustness and accuracy by considering noise and data core properties for better hyperbox representation.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • Traditional fuzzy min-max neural networks (FMNN) face challenges with noise and overlapping data.
  • Existing FMNN models may not optimally represent complex data distributions.

Purpose of the Study:

  • To introduce a Data Core Fuzzy Min-Max Neural Network (DCFMN) for robust pattern classification.
  • To enhance FMNN by incorporating data core concepts and a new membership function.

Main Methods:

  • Developed a new membership function considering noise, geometric center, and data core.
  • Introduced overlapped neurons to represent inter-class hyperbox overlaps.
  • Presented online learning and classification algorithms tailored for DCFMN.

Main Results:

  • DCFMN demonstrates strong robustness and high classification accuracy.
  • Performance evaluation on benchmark datasets shows superiority over traditional FMNN variants.
  • DCFMN achieved excellent results in pipeline pattern classification tasks.

Conclusions:

  • The proposed DCFMN effectively handles noise and data core characteristics for improved classification.
  • DCFMN offers a significant advancement in fuzzy neural network pattern classification.
  • The model shows excellent potential for real-world pattern recognition applications.