一种基于混沌系统不稳定周期轨道的数据分类识别方法
By constructing a chaotic mapping system and adding a time-series control signal, and using symbolic dynamics to classify data, the problems of excessive human intervention and low classification accuracy in existing technologies are solved, and efficient data classification and storage are achieved.
CN118940106BActive Publication Date: 2026-07-17CHINA INFOMRAITON CONSULTING & DESIGNING INST CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA INFOMRAITON CONSULTING & DESIGNING INST CO LTD
- Filing Date
- 2024-07-22
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing data classification methods involve too much human intervention, resulting in low classification accuracy and poor portability.
Method used
By constructing a chaotic mapping system, an initial chaotic pulse signal is generated and a timing control signal is added to obtain a stable periodic orbit. The orbit is then mapped into a symbol sequence using a symbol dynamics method, and control rules are formulated for pattern recognition to achieve data classification.
Benefits of technology
It improves the accuracy and reliability of data classification, enhances information processing capabilities in complex environments, and improves computing efficiency and data storage capacity.
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Figure CN118940106B_ABST
Abstract
本发明公开了一种基于混沌系统不稳定周期轨道的数据分类识别方法，包括：采集计算机系统中待分类的原始数据，建立数据集；构建混沌映射系统，产生初始混沌脉冲信号，用于对所述数据集进行映射；在混沌映射系统中加入时序控制信号，对所述映射后的数据集进行控制，得到稳定周期轨道；采用符号动力学方法，将所述稳定周期轨道映射成符号序列；制定控制规则，将符号序列调整为最终的混沌脉冲信号；基于最终的混沌脉冲信号，对数据集进行模式分类识别。本发明通用性强，可用于大数据分级分类、图像识别分类、语音处理分类、生物信息学等场景下。
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