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Study of Principle Component Analysis and Learning Vector Quantization Genetic Neural Networks | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 27, Issue 2, 2009, Pages 321-331 PDF (302.15 K) | ||
| DOI: 10.30684/etj.27.2.10 | ||
| Authors | ||
| Mazin Z. Othman; Arif A. Al-Qassar | ||
| 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 | ||
| neural networks; Genetic algorithms; Principle Component Analysis; Learning Vector Quantization; Signature Recognition | ||
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