Magister

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/60

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    Diagnostic des systèmes complexes par les réseaux de neurones et les algorithmes génétiques : application à un système de trois réservoirs DTS-200
    (2009) Beddek, Karim
    Since their existence, human beings are always in conflict with the nature that surrounds them. But with their willingness and determination, people have succeeded in overcoming the aforementioned conflicts by getting inspired from other ceatures which have adopted certain lifestyle and behavior that would help them to live and make life easier. For example, it is from how birds build their nests and how life is organized in kingdoms of bees and ants that people extracted some social and organizational skills to help themselves overcome their daily difficulties. After discoverig the huge capabilities of human minds, people started to make of their basic concepts and understandings artificial minds. Furthermore, people started to think of ways to mantain things that are strong and durable. For this reason they started to model these target attributes in the form of genes (chromosomes) in the chromosome so that they can be kept from one generation to another until the removal of all things that are not appropriate to the planned needs. In this context, the focus of our work, where we have set up the control system of a system called the DTS-200 and the hybridization of artificial neural networks and genetic algorithms. Based on the DTS-200 system we have used neural networks, which are considered as the most approximate global solutions for neural models. To find this neural model, we used genetic algorithms in the exploration mechanisms of the neural networks, in that it modeled the factors as the square difference between the real and the Neural model. We used in this algorithm a set of individuals where everyone represents a special structure of the device carrying the genes used in the neural network. To speed up the process of exploration, we have crossed the genetic algorithm with the Newton method. The obtained neural model was applied for the detection of defects in this organ, in the case of normal operation we count the intervals of confidence, if the standard deviation of the signal is outside of these intervals during the operation; we can deduce that there is an imbalance in the system, which we have to locate. To locate the defect we used another neural network which is crossed with the genetic algorithm in the same way as the first, and every output of the neural outputs represents a certain misfunction
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    Multimodal biometric fusion using evolutionary techniques
    (2014) Hafnaoui, Imane
    The work of this research focuses on fusing multiple biometric modalities at the score level using different combination rules. The research puts an emphasis on employing optimization techniques in order to achieve optimum accuracies. Due to the limitations that unimodal systems suffer from, such as noisy data, non-universality, and susceptibility to spoof attacks, multibiometric systems have gained much interest in the research community on the grounds that they alleviate most of these limitations and are capable of producing better accuracies and performances. A multibiometric system combines two or more biometric sources in order to overcome their unimodal system counterparts and achieve higher accuracies. One of the important steps to reach this purpose is the choice of the fusion techniques utilized. A thorough study is performed to investigate the different fusion rules and schemes. In this work, a modeling step based on a hybrid algorithm that includes social rules derived from the swarm intelligence, Particle Swarm Optimization, and the concepts of natural selection and evolution, Genetic Algorithm, is used to combine the two modalities at the score level. This optimization algorithm is employed to select the optimum weights associated to the modalities being fused. The performance of the hybrid GA-PSO is compared to those of classical combination rules. For that purpose, the proposed schemes are experimentally evaluated on publicly available score databases (XM2VTS, NIST and BANCA) which come in clean and degraded conditions. An analysis of the results is carried out on the basis of comparing the techniques' resulting EER accuracies and ROC curves. Furthermore, the execution speed of the hybrid approach is compared to that of the single optimization algorithms GA and PSO
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    Use of genetic algorithms in antennas. Application to Yagi-Uda antenna and antenna arrays
    (2006) Recioui, Abdelmadjid
    Les algorithmes génétiques sont utilisés pour optimiser la performance de l'antenne pour différents objectifs. En premier lieu, les antennes de type Yagi-Uda sont optimisées pour le gain, le niveau de lobes secondaires, et l'impédance entrée en utilisant des algorithmes génétiques. Ensuite, les algorithmes génétiques couplés avec la méthode de synthèse de Schelkunoff sont employés dans la synthèse des réseaux linéaires équidistants. Les différentes amplitudes et phases d'excitations sont obtenues pour réaliser un bon assortiment avec un diagramme de rayonnement désiré. L'approche utilisée dans la synthèse des réseaux linéaires est étendue à la synthèse des réseaux plans en utilisant le principe de séparation. Des exemples qui démontrent la polyvalence de l'approche présentée dans ce travail sont inclus pour différentes formes de diagrammes de rayonnement y compris le cas du diagramme orientées