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Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic systems that exhibit spiking behaviors and can be designed using evolutionary optimization. Array elements are rapidly reconfigurable and can function as either neurons, synapses or fan-out elements with programmable interconnections and parameters. Currently, DANNAs are implemented using Field Programmable Gate Arrays (FPGAs) and are...
In order to facilitate online training of a robot controller composed of a spiking neural network, we propose the creation of a method dubbed a `compact evolutionary algorithm'. The compact evolutionary algorithm, derived from the compact genetic algorithm, greatly reduces the memory requirements for evolutionary optimization and also obviates the need for floating-point arithmetic capabilities allowing...
This paper describes a multimodal methodology for evolutionary optimization of neural networks. In this approach, we use Differential Evolution with parallel subpopulations to simultaneously train a neural network and find an efficient architecture. The results in three classification problems have shown that the neural network resulting from this method has low complexity and high capability of generalization...
The problem of parametrical synthesis of neural network models is considered. The evolutionary method with using the aprioristic information for neural network training is developed. Experiments on neural models synthesis for medical diagnostics are lead.
The oil industries in the entire World and particularly in Mexico, have been taking an important relevance. There are two major challenges in this industry. The first one is the exploration and utilization of crude oil in deep sea, the second one is the scarce of light crude, the actual production report an increment of heavy crude, generating corrosion steel in the extraction and refinement processes...
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