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 Experiment Videos

An improved genetic algorithm based fuzzy-tuned neural network.

S H Ling1, F H F Leung, H K Lam

  • 1Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. ensteve@eie.polyu.edu.hk

International Journal of Neural Systems
|December 31, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A generalized algorithm for the automatic diagnosis of sleep apnea from per-sample encoding of airflow and oximetry.

Physiological measurement·2022
Same author

A novel algorithm for automatic diagnosis of sleep apnea from airflow and oximetry signals.

Physiological measurement·2020
Same author

Fault detection for T-S fuzzy time-delay systems: delta operator and input-output methods.

IEEE transactions on cybernetics·2014
Same author

Predicting protein-ligand binding site using support vector machine with protein properties.

IEEE/ACM transactions on computational biology and bioinformatics·2014
Same author

Computational intelligence in health technologies.

International journal of bioinformatics research and applications·2013
Same author

Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2011
Same journal

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026
Same journal

Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.

International journal of neural systems·2026
Same journal

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026
Same journal

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

International journal of neural systems·2026
Same journal

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

International journal of neural systems·2026
Same journal

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

International journal of neural systems·2026
See all related articles

A novel fuzzy-tuned neural network, trained by an improved genetic algorithm (GA), demonstrates superior performance over traditional neural networks. This enhanced approach offers greater flexibility and efficiency in complex modeling tasks.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Traditional neural networks often have limitations in adaptability and function approximation.
  • Neural-fuzzy systems offer enhanced control and parameter tuning capabilities.
  • Genetic algorithms (GAs) are effective optimization tools but can be improved for complex training tasks.

Purpose of the Study:

  • To introduce a novel fuzzy-tuned neural network architecture.
  • To develop an improved genetic algorithm (GA) for training the proposed network.
  • To demonstrate the superior performance of the proposed methods compared to traditional approaches.

Main Methods:

  • A modified neuron model with two activation functions was employed to increase the network's functional degree of freedom.

Related Experiment Videos

  • A neural-fuzzy network was integrated to dynamically govern parameters of the modified neuron model.
  • An improved genetic algorithm (GA) with enhanced genetic operations was developed for network training.

    • Main Results:

    • The proposed fuzzy-tuned neural network exhibited better performance than traditional neural networks with comparable parameter counts.
    • The improved genetic algorithm (GA) demonstrated superior performance in training the network compared to the traditional GA.
    • Application examples validated the effectiveness of the proposed neural network and GA.

    Conclusions:

    • The fuzzy-tuned neural network offers enhanced performance and flexibility.
    • The improved genetic algorithm (GA) provides a more effective training mechanism.
    • The combined approach presents a powerful tool for complex computational problems.