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Titlebook: Engineering Applications of Neural Networks; 12th International C Lazaros Iliadis,Chrisina Jayne Conference proceedings 2011 IFIP Internati

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Share the Recipe and Teach the Mealcond. Compared to a software implementation on a workstation, our solution achieves the same classification performance (93.3% hit rate), with more than twice the throughput and more than an order of magnitud less power.
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Television, Film and Media Education,orithm is presented along with results from a large real-world European credit card data set. Through this application it is shown that neural networks can assist in mission-critical areas of business and are an important tool in the transparent detection of fraud.
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https://doi.org/10.1007/978-3-030-13012-1sed appropriately. Furthermore, such environments are not static. Therefore, the robot needs to learn novel objects. In this paper, we propose a method for learning and identifying obstacles based on multi-modal information. As this approach is based on Adaptive Resonance Theory networks, it is inherently capable of incremental online learning.
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and non-navigable regions. It also uses supervised learning techniques which work with different levels of memory of the templates. As output our system is capable controlling speed and steering for autonomous mobile robot navigation. Experimental tests have been carried out to evaluate the learning techniques.
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