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Two experiments have been proposed for evaluating a GA-based approach, using error rate (Fuentes G, et. GA have been applied to digital images regarding recognition and quality control, using different features (texture, statistics, colour and other descriptors and transforms). Some approaches have proposed effective feature selection, aimed at reducing feature dimension and having minimum classification errors. Daza G.S, Sánchez L.G, 2007 Rivera J.H, Castellanos C, 2007 Changjing S, Barnes D, 2009) selection has been limited to a number of specific characteristics, one-dimensional signals or face recognition tasks whilst GA has been used to tune other techniques' parameters. The proposed GA approach's performance was evaluated by using fitness functions, classifiers and compared to other research.Ī GA optimises a fitness function and (in this case) determines the best subset of features. This work was focused on the selection stage by using a genetic algorithm (GA) approach the classification stage involved a neural network-based approach. Stavrakoudis, 2010 Vilches E, 2006 Pazoki Z, Farokhi F, 2010). However, the three processes involved in pattern recognition can be developed by using computational intelligence, thereby providing an autonomous system having learning ability and suitable classification results, according to recent research (Mitra S. Pattern recognition has been developed using classical statistical methods such as principal component analysis (PCA) (Song F, Guo Z, 2010) and independent component analysis (ICA) (Ekenel H, Sankur B, 2004 Liu J, Wang G, 2010). & Koutroumbas, K, 2009) for identifying a given object and performing operations on it. Pattern recognition has become an integral part of most machine vision systems' output regarding decision-making (Theodoridis S. This trend has earned pattern recognition a central place in research and engineering applications. Technological advances in our society have enhanced industrial production automation and the need for handling information in fields of great importance. Received: March 23rd 2012 Accepted: March 12th 2013 Palabras clave: Algoritmos Genéticos, Vector De Características, Redes Neuronales, Reconocimiento de Patrones. Los resultados obtenidos muestran un mejor desempeño del sistema propuesto frente a otros métodos. La red neuronal se compara con el clasificador de los k-vecinos. Se implementan redes neuronales en la clasificación usando las características seleccionadas por el algoritmo genético. Para evaluar la propuesta con algoritmos genéticos se utilizan dos funciones de evaluación: tasa de error y coeficiente Kappa. En el siguiente artículo se presenta un enfoque para selección de características utilizando un algoritmo genético aplicado a procesos de reconocimiento y control de calidad en imágenes. Keywords: Feature vector, genetic algorithm, neural network, pattern recognition.Įl desempeño en el reconocimiento de patrones depende de las variaciones en las etapas de extracción, selección y clasificación. The proposed approach performed better than the other methods. The neural network approach was compared to a K-nearest neighbour classifier. Error rate and kappa coefficient were used for evaluating the genetic algorithm approach Neural networks were used for classification, involving the features selected by the genetic algorithms. This paper presents an approach to feature selection by using genetic algorithms with regard to digital image recognition and quality control. Pattern recognition performance depends on variations during extraction, selection and classification stages. G., Using genetic algorithm feature selection in neural classification systems for image pattern recognition., Ingeniería e Investigación.
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#Programación de piezas feature cam full
Affiliation: Full time professor in the Electrical and Electronics Engineering Department and Program Coordinator for the Master's degree in Electronics Engineering at Universidad del Norte, Barranquilla (Colombia). Tecnologías de la Información, Universidad de Girona. Electronics and Telecommunications Research Group Coordinator, Universidad Autónoma del Caribe, Barranquilla (Colombia). Affiliation: Lecturer in the Electrical and Electronics Engineering Department, Universidad del Norte. Ingeniería Electrónica - Ingeniería Eléctrica Universidad del Norte. Ingeniería Electrónica Universidad del Norte. Implementación de selección de características con algoritmos genéticos en clasificadores neuronales para reconocimiento de patrones en imágenes Using genetic algorithm feature selection in neural classification systems for image pattern recognition