Study of Principle Component Analysis and Learning Vector Quantization Genetic Neural Networks - Mazin Z. Othman; Arif A. Al-Qassar (Engineering and Technology Journal, 2009)
In this work, the Genetic Algorithm (GA) is used to improve the performance ofLearning Vector Quantization Neural Network (LVQ-NN), simulation results show thatthe GA algorithm works well in pattern recognition field and it converges much fasterthan conventional competitive algorithm. Signature recognition system using LVQ-NNtrained with the competitive algorithm or genetic algorithm is proposed. This schemeutilizes invariant moments adopted for extracting feature vectors as a preprocessing ofpatterns and a single layer neural network (LVQ-NN) for pattern classification. A verygood result has been achieved using GA in this system. Moreover, the PrincipleComponent Analysis Neural Network (PCA-NN) which its learning technique isclassified as unsupervised learning is also enhanced by hybridization with the geneticalgorithm. Three algorithms were used to train the PCA-NN. These are GeneralizedHebbian Algorithm (GHA), proposed Genetic Algorithm and proposed HybridNeural/Genetic Algorithm (HNGA).