论文标题
位置优化器：自然风格的优化算法
Position-wise optimizer: A nature-inspired optimization algorithm
论文作者
论文摘要
人类神经系统利用突触可塑性来解决优化问题。先前的研究试图将可塑性因子添加到人工神经网络的训练过程中，但是大多数模型都需要对网络或复杂的新颖规则进行复杂的外部控制。在此手稿中，引入了一种新型自然风格的优化算法，该算法模仿了生物神经可塑性。此外，该模型在三个数据集上进行了测试，并将结果与梯度下降优化进行了比较。
The human nervous system utilizes synaptic plasticity to solve optimization problems. Previous studies have tried to add the plasticity factor to the training process of artificial neural networks, but most of those models require complex external control over the network or complex novel rules. In this manuscript, a novel nature-inspired optimization algorithm is introduced that imitates biological neural plasticity. Furthermore, the model is tested on three datasets and the results are compared with gradient descent optimization.
