Nowadays, artificial neural networks have been successfully applied on several research and application fields. An appropriate configuration for a neural network is a complex task, and it often requires the knowledge of an expert on the application. A technique for automatic configuration for a neural network is formulated as an optimization problem. Two strategies are considered: a mono-objective minimization problem, using multi-particle collision algorithm (MPCA); and a multi-objective minimization problem addressed by the non-dominated sorting genetic algorithm (NSGA-II). The proposed optimization approaches were tested for two application in geosciences: data assimilation for wave evolution equation, and the mesoscale seasonal climate prediction for precipitation. Better results with automatic configuration were obtained for data assimilation than those obtained by network defined by an expert. For climate seasonal precipitation, automatic configuration presented better predictions were presented than ones carried out by an expert. For the worked examples, the NSGA-II presented a superior result for the worked experiments.

中文翻译：

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优化神经网络架构的两个地球科学应用

目前，人工神经网络已成功应用于多个研究和应用领域。神经网络的适当配置是一项复杂的任务，它通常需要应用程序专家的知识。神经网络的自动配置技术被表述为优化问题。考虑了两种策略：单目标最小化问题，使用多粒子碰撞算法（MPCA）；以及由非支配排序遗传算法 (NSGA-II) 解决的多目标最小化问题。建议的优化方法在地球科学的两个应用中进行了测试：波动演化方程的数据同化和降水的中尺度季节性气候预测。与通过专家定义的网络获得的数据同化相比，自动配置的数据同化获得了更好的结果。对于气候季节性降水，自动配置提供了比专家进行的更好的预测。对于工作示例，NSGA-II 为工作实验提供了优越的结果。