Statistical estimation and modified learning vector quantization based flexible fuzzy neural networks with applications

Authors:
Mohamed S. Ibrahim;Ahmed Mohamed
Affiliations:
Knowledge Based Systems and Robotic Department, Mubarak City for Scientific Research and Technology Applications, Egypt;Networks and Distributed Systems Department, Mubarak City for Scientific Research and Technology Applications, Egypt
Venue:
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Year:
2006

Citing 6
Cited 0

The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network

Computer
Self-organization and associative memory: 3rd edition

Self-organization and associative memory: 3rd edition
Designing a layered network for context sensitive pattern classification

Neural Networks
A self-organising network that grows when required

Neural Networks - New developments in self-organizing maps
A reinforcement learning adaptive fuzzy controller for robots

Fuzzy Sets and Systems - Theme: Modeling and control
An on-line algorithm for creating self-organizing fuzzy neural networks

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a flexible multidimensional fuzzy neural network (FMFN). The proposed network is based on the integration of the modified learning vector quantization (MLVQ) and the statistical estimation(SE). The first method is developed to modify the fuzzy receptive field neuronal (FRFN) centroid vector. The second method is proposed and developed to estimate the neuronal variances. Simulation results shows that the proposed network are generated and constructed automatically and tested for approximating highly nonlinear surface and controlling a nonlinear dynamical system. Comparative anaylsis with other fuzzy neural networks are performed and discussed.