Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

Authors:
Gang Leng;T. M. McGinnity;G. Prasad
Affiliations:
Sch. of Informatics, Manchester Univ.;-;-
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2006

Citing 0
Cited 24

Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter

Fuzzy Sets and Systems
Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems

Information Sciences: an International Journal
Quadratic optimal neural fuzzy control for synchronization of uncertain chaotic systems

Expert Systems with Applications: An International Journal
Global and Local Modelling in Radial Basis Functions Networks

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A novel hybrid algorithm for creating self-organizing fuzzy neural networks

Neurocomputing
Control of a rope-driven self-leveling device for leveling adjustment

ACC'09 Proceedings of the 2009 conference on American Control Conference
Genetic algorithms for MLP neural network parameters optimization

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Damping of low frequency oscillations in power system network using swarm intelligence tuned fuzzy controller

International Journal of Bio-Inspired Computation
eFSM: a novel online neural-fuzzy semantic memory model

IEEE Transactions on Neural Networks
A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control

Expert Systems with Applications: An International Journal
Dynamic identification of Staubli RX-60 robot using PSO and LS methods

Expert Systems with Applications: An International Journal
Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach

Expert Systems with Applications: An International Journal
Knowledge acquisition in fuzzy-rule-based systems with particle-swarm optimization

IEEE Transactions on Fuzzy Systems
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm

IEEE Transactions on Fuzzy Systems
The TaSe-NF model for function approximation problems: Approaching local and global modelling

Fuzzy Sets and Systems
A dynamic threshold decision system for stock trading signal detection

Applied Soft Computing
A Generalized Ellipsoidal Basis Function Based Online Self-constructing Fuzzy Neural Network

Neural Processing Letters
Self-adaptive interval type-2 neural fuzzy network control for PMLSM drives

Expert Systems with Applications: An International Journal
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system

Applied Soft Computing
A structure optimisation algorithm for feedforward neural network construction

Neurocomputing
New Online Self-Evolving Neuro Fuzzy controller based on the TaSe-NF model

Information Sciences: an International Journal
Fuzzy based genetic neural networks for the classification of murder cases using Trapezoidal and Lagrange Interpolation Membership Functions

Applied Soft Computing
Data driven modeling based on dynamic parsimonious fuzzy neural network

Neurocomputing
Efficient self-organizing multilayer neural network for nonlinear system modeling

Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the epsiv-completeness of fuzzy rules is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules. This has two steps: First, the linear parameter matrix is adjusted, and second, the centers and widths of all membership functions are modified. The GA is introduced to identify the least important neurons, i.e., the least important fuzzy rules. Simulations are presented to illustrate the performance of the proposed algorithm