| 仿生模式识别(拓扑模式识别)——一种模式识别新模型的理论与应用 |
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| 引用本文： | 王守觉.仿生模式识别(拓扑模式识别)——一种模式识别新模型的理论与应用[J].电子学报,2002,30(10):1417-1420. |
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| 作者姓名： | 王守觉 |
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| 作者单位： | 中国科学院半导体研究所神经网络实验室,北京,100083 |
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| 基金项目： | 国家自然科学基金项目 (No 60 1 350 1 0 ) |
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| 摘 要： | 本文提出了一种模式识别理论的新模型,它是基于"认识"事物而不是基于"区分"事物为目的.与传统以"最佳划分"为目标的统计模式识别相比,它更接近于人类"认识"事物的特性,故称为"仿生模式识别".它的数学方法在于研究特征空间中样本集合的拓扑性质,故亦称作"拓扑模式识别"."拓扑模式识别"的理论基点在于它确认了特征空间中同类样本的连续性(不能分裂成两个彼此不邻接的部分)特性.文中用"仿生模式识别"理论及其"高维空间复杂几何形体覆盖神经网络"识别方法,对地平面刚体目标全方位识别问题作了实验.对各种形状相像的动物及车辆模型作全方位8800次识别,结果正确识别率为99.75%,错误识别率与拒识率分别为0与0.25%.
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| 关 键 词： | 模式识别 神经网络 仿生学 高维几何 |
| 文章编号： | 0372-2112(2002)10-1417-04 |
| 作者简介： | 王守觉,男,1925年生于上海,早年就读于西南联大和同济大学,毕业后在北平研究院镭学研究所从事氧化亚铜研究,解放后改为中国科学院应用物理所结晶学室,1960年成立半导体所后历任器件室主任、副所长、所长等职务,1980年当选为学部委员(院士),现为中国电子学会副理事长,《电子学报》主编,中国半导体学科奠基人之一,现从事半导体超高速电路与人工神经网络算法、模型、硬件和应用的研究. |
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Bionic (Topological) Pattern Recognition—— A New Model of Pattern Recognition Theory and Its Applications |
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| WANG Shou-jue.Bionic (Topological) Pattern Recognition—— A New Model of Pattern Recognition Theory and Its Applications[J].Acta Electronica Sinica,2002,30(10):1417-1420. |
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| Authors: | WANG Shou-jue |
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| Affiliation: | Topological |
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| Abstract: | A new model of pattern recognition principles,witch is based on "matter cognition"instead of "matter classification"in traditional statistical pattern recognition,has been proposed.This new model is better closer to the function of human being,rather than traditional statistical pattern recognition using"optimal seperating"as its main principle.So the new model of pattern recognition is called the Bionic Pattern Recognition.Its mathematical basis are topological analysis of sample set in the high dimensional feature space,therefore it is also called the Topological Pattern Recognition.The basic idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples.We did experiments on recognition of omnidirectionally oriented rigid objects on the same level,with the Bionic Pattern Recognition using neural networks,which acts by the method of covering the high dimensional geometrical distribution of the sample set in the feature space.Many animal and vehicle models(even with rather similar shapes) were recognized omnidirectionally thousands of times.For total 8800 tests,the correct recognition rate is 99.75%,the error rate and the rejection rate are 0 and 0.25 respectively. |
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| Keywords: | pattern recognition neural networks bionics high dimensional geometry |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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