应用状态描述方法(SRM)的心音信号识别

介绍了一种新的心音信号识别方法——状态描述方法(SRM),它不仅能有效区分正异常心音,还能对同一个人不同时期的心音状态进行评估。利用SRM分析心音信号,通过传统的方法提取出心音信号的特征值后,构建出SRM模型。实验结果表明:通过该模型可以有效区分不同状态的心音信号。区别于传统心音识别方法,SRM通过尺度的变化能不同程度地显示心音的状态变化,为研究生物信号模式识别提供了一种新的思路。

2个中药品种常数及多种中药配对的精确分类鉴别

目的:基于双指标信息理论方程-共有遗传与变异信息理论方程最大信息量状态,提出2个鉴别生物复杂体系中药的品种常数:共有峰率P_g=61%,P_g=70%作为中药品种的绝对定量鉴别标准。在双指标信息量变化相对误差±3%的范围内,可以确定P_g≥(61±3)%及P_g≥(70±3)%作为2个优化判别中药品种的定量理论标准。方法:基于提出的2个定量理论标准,结合最大有效样本数优化法对多个中药样本进行合理模式识别。测试并分析了4种42个中药复方桂附地黄丸、知柏地黄丸、金匮肾气丸、明目地黄丸样品原药粉末的红外指纹图谱,对4种不同复方的6个配对进行了鉴别验证。结果:5个中药复方配对遵从品种常数P_g=61%,1个复方配对桂附地黄丸、知柏地黄丸遵从品种常数P_g=70%,样品的正确识别率为94. 2%,类的正确识别率100%。结论:该法提供了鉴别分类中药的科学的绝对定量标准。

基于竞争聚类的离线签名验证算法

提出一种新的基于竞争聚类的离线签名验证算法。在注册阶段,通过样本特征的相关系数检查样本的一致性;在验证阶段,利用样本与待验签名的竞争聚类结果判断待验签名的真伪。实验结果表明,该算法不需用阈值来进行控制,能有效地降低误纳率。

A biologically inspired model for pattern recognition

In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.