Control of Traffic Light in Isolated Intersections Using Fuzzy Neural Network and Genetic Algorithm

Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.

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Integration of a fuzzy neural network and multi-objective genetic algorithm for optimisation of BLU light guide plate injection moulding parameters

International Journal of Materials and Product Technology ◽

10.1504/ijmpt.2012.051344 ◽

2012 ◽

Vol 45 (1/2/3/4) ◽

pp. 83

Author(s):

Shen Tsu Wang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Injection Moulding ◽

Fuzzy Neural Network ◽

Light Guide ◽

Multi Objective ◽

Light Guide Plate ◽

Guide Plate ◽

Multi Objective Genetic Algorithm ◽

Fuzzy Neural

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Modeling and optimization for the secondary reaction of FCC gasoline based on the fuzzy neural network and genetic algorithm

Chemical Engineering and Processing - Process Intensification ◽

10.1016/j.cep.2006.05.011 ◽

2007 ◽

Vol 46 (3) ◽

pp. 175-180 ◽

Cited By ~ 18

Author(s):

Zhiwen Wang ◽

Bolun Yang ◽

Chun Chen ◽

Jun Yuan ◽

Longyan Wang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fuzzy Neural Network ◽

Secondary Reaction ◽

Modeling And Optimization ◽

Fcc Gasoline ◽

Fuzzy Neural

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A design for a self-organizing fuzzy neural network based on the genetic algorithm

SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483) ◽

10.1109/icsmc.2003.1244700 ◽

2004 ◽

Cited By ~ 4

Author(s):

G. Leng ◽

T.M. McGinnity ◽

G. Prased

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fuzzy Neural Network ◽

Fuzzy Neural ◽

Self Organizing

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Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.33-37.1283 ◽

2008 ◽

Vol 33-37 ◽

pp. 1283-1288 ◽

Cited By ~ 2

Author(s):

Chao Hua Fan ◽

Yu Ting He ◽

Hong Peng Li ◽

Feng Li

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Aluminum Alloy ◽

Aluminum Alloys ◽

Performance Prediction ◽

Bp Neural Network ◽

Fuzzy Neural Network ◽

Neural Network Method ◽

Network Method ◽

Fuzzy Neural

Genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada. At the same time, a fuzzy-neural network method is established for the same purpose. The results indicate that genetic algorithm-neural network and fuzzy-neural network can both be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely.

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THE PROBLEM OF MODELLING A TRUNK AIR ROUTE NETWORK

Aviation ◽

10.3846/16487788.2013.774939 ◽

2013 ◽

Vol 17 (1) ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Tatiana Tseytlina ◽

Victor Balashov ◽

Andrey Smirnov

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fuzzy Neural Network ◽

Development Environment ◽

Route Network ◽

Information Models ◽

The Neural Network ◽

Fuzzy Neural ◽

Neural Network Toolbox

In this work we developed a fuzzy neural network-based model of the conditions for the existence of air routes, i.e. the rules underlying the emergence, existence and elimination of air routes (direct links between cities). The model belongs to the class of information models: the existence or non-existence of an air route is considered dependent on a complex of parameters. These parameters characterise the transport link, as well as the generational and target capabilities of the connected cities. The model was constructed using genetic algorithm techniques and self-organising Kohonen maps (implemented by software features of the STATISTICA package), as well as software tools of the Fuzzy Logic Toolbox and the Neural Network Toolbox of the MatLab development environment. The model is used to forecast the development of the topology of the network. The forecast is a necessary component of long-term forecasts of demand in the aircraft market.

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