Analog-Digital Self-Learning Fuzzy Spiking Neural Network and Its Learning Algorithm Based on ‘Winner-Takes-More’ Rule

Radio Electronics, Computer Science, Control
doi 10.15588/1607-3274-2010-1-20
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Zaporizhzhia National Technical University

Related search

First Error-Based Supervised Learning Algorithm for Spiking Neural Networks

Frontiers in Neuroscience
Neuroscience
2019English

Adaptive Fuzzy Spiking Neural P Systems for Fuzzy Inference and Learning

International Journal of Computer Mathematics
Computational TheoryApplied MathematicsComputer Science ApplicationsMathematics
2013English

Hybrid Approaches Tabu Learning Algorithm Based Neural Network

2008English

Reliability Evaluation of Distribution Network Based on Fuzzy Spiking Neural P System With Self-Synapse

Journal of Membrane Computing
2020English

A Curiosity-Based Learning Method for Spiking Neural Networks

Frontiers in Computational Neuroscience
NeuroscienceCellularMolecular Neuroscience
2020English

A Consideration on the Learning Algorithm of Neural Network

Transactions of the Society of Instrument and Control Engineers
1998English

Cooperative Coevolution for Learning Fuzzy Rule-Based Systems

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
2002English

An Efficient Fuzzy Optimization Algorithm Based on Convolutional Neural Network

MATEC Web of Conferences
Materials ScienceEngineeringChemistry
2020English

Multiplierless Digital Learning Algorithm for Cellular Neural Networks

IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
2001English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy