Fuzzy Neural Network Learning Model for Image Recognition

H. Adeli*, Shih-Lin Hung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

An unsupervised fuzzy neural network classification algorithm has been developed and applied to perform feature abstraction and classify a large number of training instances into a small number of clusters. A fuzzy neural network learning model has been developed by integrating the unsupervised fuzzy neural network classification algorithm with a genetic algorithm and an adaptive conjugate gradient neural network learning algorithm. The learning model has been applied to the domain of image recognition. The performance of the model has been evaluated by applying it to a large-scale training example with 2304 training instances. An average computational speedup of eight is achieved by the new algorithm.

Original languageEnglish
Pages (from-to)43-55
Number of pages13
JournalIntegrated Computer-Aided Engineering
Volume1
Issue number1
DOIs
StatePublished - 1 Jan 1993

Fingerprint Dive into the research topics of 'Fuzzy Neural Network Learning Model for Image Recognition'. Together they form a unique fingerprint.

Cite this