Authors
Abstract
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).
Keywords
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