基于混沌概率分析的优化分类数学模型仿真

Simulation on Optimization Classification Mathematic Model Based on Chaotic Probability Analysis

在数据准确优化分类和数学模型建立及仿真问题的研究中。传统的数据分类由于频率点的集中不适合进行数据分类频点的采样去除,低自适应能力的调频节点分类技术对干扰或者处于深度衰落部位的分类节点频段拒绝使用,从而大大限制了分类的准确性和抗干扰能力。为此提出基于混沌概率分析的优化分类数学模型建立及仿真,通过混沌概率分析生成具有反应数据显著本质特征的特定随机数产生序列,满足之前概率密度的随机调频需求,由此实现对数据分类的优化和数学模型的构建。以原始采集到的某辐射噪声场实测数据集样本为实验数据,进行数学建模与仿真实验,仿真实验表明,分类数学模型能有效对各类数据进行分类重组,分类效果显著,可以有效应用与数据挖掘,故障诊断以及目标识别等分类识别领域。

The data precise clustering and classification and its mathematic model building problem was researched and simulated in this paper. An improved and optimized data classification mathematic model was proposed based on the chaotic probability analysis. The classification error rates was mapped as a probability density function based on the channel mapping function method, The data clustering and optimization classification was realized from now on. Simula-tion was taken with the real collected data from the radiated noise data, and the mathematic model was built in the ex-periment, result shows that the proposed method can classify each type of the data effectively. The performance of the data classification is perfect, it shows that the model and algorithm has excellent classification performance and can be taken in the application such as data mining, fault diagnosis and target recognition and so on.