937KBZIP

KAN+LSTM Time Series Prediction (Python full source code and data)

Pytorch版本，运行主程序mult即可。

KAN作为这两年最新提出的机制，目前很少人用，很适合作为时间序列预测的创新点，可以结合常规的网络加上个优化方法做创新。适合功率预测，负荷预测，流量预测，浓度预测，机械领域预测等等各种时间序列预测。

KAN（Kolmogorov–Arnold Networks）的模型，它对标的是MLPs（多层感知机），这个模型由数学定理Kolmogorov–Arnold启发得出的。该模型最重要的一点就是把激活函数放在了权重上，也就是在权重上应用可学习的激活函数，这些一维激活函数被参数化为样条曲线，从而使得网络能够以一种更灵活、更接近Kolmogorov-Arnold 表示定理的方式来处理和学习输入数据的复杂关系。

Resource Download

The download price for this resource is16.0Gold coins, please firstlog in

Resource Disclaimer (Purchase is deemed to be agreement with this statement):
1. Any operation on the website platform is considered to have read and agreed to the registration agreement and disclaimer at the bottom of the website, this site resources have been ultra-low price, and does not provide technical support
2. Some network users share the net disk address may be invalid, such as the occurrence of failure, please send an e-mail to customer service code711cn#qq.com (# replaced by @) will be made up to send
3. This site provides all downloadable resources (software, etc.) site to ensure that no negative changes; but this site can not guarantee the accuracy, security and integrity of the resources, the user downloads at their own discretion, we communicate to learn for the purpose of not all the source code is not 100% error-free or no bugs; you need to have a certain foundation to be able to read and understand the code, be able to modify the debugging yourself! code and solve the error. At the same time, users of this site must understand that the Source Code Convenience Store does not own any rights to the software provided for download, the copyright belongs to the legal owner of the resource.
4. All resources on this site only for learning and research purposes, please must be deleted within 24 hours of the downloaded resources, do not use for commercial purposes, otherwise the legal disputes arising from the site and the publisher of the collateral liability site and will not be borne!
5. Due to the reproducible nature of the resources, once purchased are non-refundable, the recharge balance is also non-refundable