Study of principle component analysis and learning vector guantzation genetic neural networks

Abstract EN

In this work, the Genetic Algorithm (GA) is used to improve the performance of Learning Vector Quantization Neural Network (LVQ-NN), simulation results show that the GA algorithm works well in pattern recognition field and it converges much faster than conventional competitive algorithm.

Signature recognition system using LVQ-NN trained with the competitive algorithm or genetic algorithm is proposed.

This scheme utilizes invariant moments adopted for extracting feature vectors as a preprocessing of patterns and a single layer neural network (LVQ-NN) for pattern classification.

A very good result has been achieved using GA in this system.

Moreover, the Principle Component Analysis Neural Network (PCA-NN) which its learning technique is classified as unsupervised learning is also enhanced by hybridization with the genetic algorithm.

Three algorithms were used to train the PCA-NN.

These are Generalized Hebbian Algorithm (GHA), proposed Genetic Algorithm and proposed Hybrid Neural/Genetic Algorithm (HNGA).