Neuronal networks are used in different fields of science and technology due to their capacity to approximate nonlinear functions through the synaptic weights optimization. This work shows a new form of optimization for neuronal networks based in fractional calculus. The fractional adaptation algorithm proposed was used to identify mechanical, electrical and biological systems. In each of the experiments a comparison between the proposed fractal-fractional model and the conventional model (with derivation order equal to one) was made.

中文翻译：

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分形神经自适应系统识别方法

由于神经元网络能够通过突触权重优化来近似非线性函数，因此它们被用于不同的科学和技术领域。这项工作显示了一种基于分数微积分的神经网络优化的新形式。提出的分数自适应算法用于识别机械，电气和生物系统。在每个实验中，对建议的分形模型与常规模型（推导阶数等于1）进行了比较。