标题： 无需超参数的神经混沌学习算法用于分类 显示英文标题

标题： Hyperparameter-Free Neurochaos Learning Algorithm for Classification

摘要： 神经混沌学习（NL）是一种受大脑启发的分类框架，它利用混沌动力学从输入数据中提取特征，并在分类任务中表现出最先进的性能。 然而，NL需要调整多个超参数并为每个输入样本计算四个混沌特征。 在本文中，我们提出AutochaosNet——NL算法的一种新型、无需超参数的变体，消除了训练和参数优化的需要。 AutochaosNet利用来自Champernowne常数的通用混沌序列，并使用输入刺激来定义特征提取的发射时间范围。 两种简化的变体——TM AutochaosNet和TM-FR AutochaosNet——与现有的NL架构——ChaosNet进行了比较。 我们的结果表明，由于计算努力的减少，AutochaosNet在显著降低训练时间的同时实现了具有竞争力或更优的分类性能。 除了消除训练和超参数调优外，AutochaosNet表现出优异的泛化能力，使其成为现实世界分类任务中可扩展且高效的选项。 未来的工作将集中在识别各种混沌映射下的通用轨道，并将它们纳入NL框架以进一步提高性能。 显示英文摘要

摘要： Neurochaos Learning (NL) is a brain-inspired classification framework that employs chaotic dynamics to extract features from input data and yields state of the art performance on classification tasks. However, NL requires the tuning of multiple hyperparameters and computing of four chaotic features per input sample. In this paper, we propose AutochaosNet - a novel, hyperparameter-free variant of the NL algorithm that eliminates the need for both training and parameter optimization. AutochaosNet leverages a universal chaotic sequence derived from the Champernowne constant and uses the input stimulus to define firing time bounds for feature extraction. Two simplified variants - TM AutochaosNet and TM-FR AutochaosNet - are evaluated against the existing NL architecture - ChaosNet. Our results demonstrate that AutochaosNet achieves competitive or superior classification performance while significantly reducing training time due to reduced computational effort. In addition to eliminating training and hyperparameter tuning, AutochaosNet exhibits excellent generalisation capabilities, making it a scalable and efficient choice for real-world classification tasks. Future work will focus on identifying universal orbits under various chaotic maps and incorporating them into the NL framework to further enhance performance.