Table of Content

Open Access

ARTICLE

Modeling the Spike Response for Adaptive Fuzzy Spiking Neurons with Application to a Fuzzy XOR

A. M. E. Ramírez-Mendoza1
CONACYT Research Fellow–State University of Nuevo León (UANL for its acronym in Spanish), Mechanical and Electrical Engineering School (FIME for its acronym in Spanish), Center for Research and Innovation in Aeronautical Engineering (CIIIA for its acronym in Spanish), Carretera a Salinas Victoria km. 2.3, Apodaca, Nuevo León, México.
*Corresponding Author: Abigail María Elena Ramírez Mendoza. Email: ; .

Computer Modeling in Engineering & Sciences 2018, 115(3), 295-311. https://doi.org/ 10.3970/cmes.2018.00239

Abstract

A spike response model (SRM) based on the spikes generator circuit (SGC) of adaptive fuzzy spiking neurons (AFSNs) is developed. The SRM is simulated in MatlabTM environment. The proposed model is applied to a configuration of a fuzzy exclusive or (fuzzy XOR) operator, as an illustrative example. A description of the comparison of AFSNs with other similar methods is given. The novel method of the AFSNs is used to determine the value of the weights or parameters of the fuzzy XOR, first with dynamic weights or self-tuning parameters that adapt continuously, then with fixed weights obtained after training, finally with fixed weights and a dynamic gain or self-tuning gain for a fine adjustment of amplitude.

Keywords

Spike response model, spikes generator circuit, fuzzy XOR, adaptive fuzzy spiking neuron, learning algorithm, fuzzy neuron, self-tuning.

Cite This Article

M., A. (2018). Modeling the Spike Response for Adaptive Fuzzy Spiking Neurons with Application to a Fuzzy XOR. CMES-Computer Modeling in Engineering & Sciences, 115(3), 295–311.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 835

    View

  • 517

    Download

  • 0

    Like

Share Link

WeChat scan