Initialization of neural network weights plays a pivotal role in determining
their performance. Feature Imitating Networks (FINs) offer a novel strategy by
initializing weights to approximate specific closed-form statistical features,
setting a promising foundation for deep learning architectures. While the
applicability of FINs has been chiefly tested in biomedical domains, this study
extends its exploration into other time series datasets. Three different
experiments are conducted in this study to test the applicability of imitating
Tsallis entropy for performance enhancement: Bitcoin price prediction, speech
emotion recognition, and chronic neck pain detection. For the Bitcoin price
prediction, models embedded with FINs reduced the root mean square error by
around 1000 compared to the baseline. In the speech emotion recognition task,
the FIN-augmented model increased classification accuracy by over 3 percent.
Lastly, in the CNP detection experiment, an improvement of about 7 percent was
observed compared to established classifiers. These findings validate the broad
utility and potency of FINs in diverse applications.

神经网络权重的初始化在决定其性能方面起着关键作用。特征模仿网络（FINs）通过将权重初始化为逼近特定的闭式统计特征，为深度学习架构奠定了有希望的基础。本研究将 FINs 的适用性扩展到其他时间序列数据集，并在比特币价格预测、语音情感识别和慢性颈痛检测三个实验中测试了模仿 Tsallis 熵以提高性能的适用性。研究结果表明，在比特币价格预测中，嵌入 FINs 的模型相比基线模型的均方根误差减少了约 1000。在语音情感识别任务中，FIN 增强模型的分类准确率提高了 3% 以上。最后，在慢性颈痛检测实验中，与已有分类器相比，观察到约 7% 的改进。这些发现验证了 FINs 在不同应用领域的广泛实用性和潜力。