There is an exhaustive study around the area of engine design that covers different methods that try to reduce costs of production and to optimize the performance of these engines. Mathematical methods based in statistics, self-organized maps and neural networks reach the best results in these designs but there exists the problem that configuration of these methods is not an easy work due the high number of parameters that have to be measured. In this work we extend an algorithm for computing implications between attributes with positive and negative values for obtaining the mixed concepts lattice and also we propose a theoretical method based in these results for engine simulators adjusting specific and different elements for obtaining optimal engine configurations.
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