论文标题
通过进化功能功能化的人工神经网络
An Artificial Neural Network Functionalized by Evolution
论文作者
论文摘要
人工神经网络的拓扑对其性能有重大影响。表征有效拓扑的是一个有前途的人工智能研究领域。但是，这并不是一项琐碎的任务，它主要是通过卷积神经网络进行的。我们提出了一个混合模型，该模型将馈送前传神经网络的张量与伪达威人的机制结合在一起。这允许找到适合阐述策略，控制问题或模式识别任务的拓扑结构。特别是，该模型可以在早期进化阶段提供适应性的拓扑，并可以在机器人，大数据和人工生活中找到应用程序。
The topology of artificial neural networks has a significant effect on their performance. Characterizing efficient topology is a field of promising research in Artificial Intelligence. However, it is not a trivial task and it is mainly experimented on through convolutional neural networks. We propose a hybrid model which combines the tensor calculus of feed-forward neural networks with Pseudo-Darwinian mechanisms. This allows for finding topologies that are well adapted for elaboration of strategies, control problems or pattern recognition tasks. In particular, the model can provide adapted topologies at early evolutionary stages, and 'structural convergence', which can found applications in robotics, big-data and artificial life.
