高校科研能力的协同IWD粗糙集-块神经网络评估模型
针对高校科研能力评估过程中存在的多因素、高非线性特点,经典评估模型主观性较强,导致模型评估准确性不高的问题,提出基于协同智能水滴算法IWD和粗糙集块神经网络RBNN的高校科研能力评估模型。首先,引入智能水滴算法,并针对传统智能水滴算法固定旁域搜索范围不利于提升算法搜索效率的问题,提出一种局部空间自动缩放算法LSAS,该算法根据当前种群最优个体,自动调整下一步搜索空间大小,对进化过程进行指导,提高算法的进化效率;其次,基于粗糙集理论对高校科研能力数据进行特征预处理,简化数据计算量;最后,对块神经网络和粗糙集参数进行编码,并对高校科研能力模型进行评估。仿真结果表明,此评估模型具有较高的准确性和较快...
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| Published in | 计算机工程与科学 Vol. 38; no. 3; pp. 486 - 493 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
首都师范大学数学科学学院,北京100048%首都师范大学数学科学学院,北京,100048
2016
滨州学院信息工程系,山东滨州256603 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1007-130X |
| DOI | 10.3969/j.issn.1007-130X.2016.03.014 |
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| Abstract | 针对高校科研能力评估过程中存在的多因素、高非线性特点,经典评估模型主观性较强,导致模型评估准确性不高的问题,提出基于协同智能水滴算法IWD和粗糙集块神经网络RBNN的高校科研能力评估模型。首先,引入智能水滴算法,并针对传统智能水滴算法固定旁域搜索范围不利于提升算法搜索效率的问题,提出一种局部空间自动缩放算法LSAS,该算法根据当前种群最优个体,自动调整下一步搜索空间大小,对进化过程进行指导,提高算法的进化效率;其次,基于粗糙集理论对高校科研能力数据进行特征预处理,简化数据计算量;最后,对块神经网络和粗糙集参数进行编码,并对高校科研能力模型进行评估。仿真结果表明,此评估模型具有较高的准确性和较快的计算效率。 |
|---|---|
| AbstractList | 针对高校科研能力评估过程中存在的多因素、高非线性特点,经典评估模型主观性较强,导致模型评估准确性不高的问题,提出基于协同智能水滴算法IWD和粗糙集块神经网络RBNN的高校科研能力评估模型。首先,引入智能水滴算法,并针对传统智能水滴算法固定旁域搜索范围不利于提升算法搜索效率的问题,提出一种局部空间自动缩放算法LSAS,该算法根据当前种群最优个体,自动调整下一步搜索空间大小,对进化过程进行指导,提高算法的进化效率;其次,基于粗糙集理论对高校科研能力数据进行特征预处理,简化数据计算量;最后,对块神经网络和粗糙集参数进行编码,并对高校科研能力模型进行评估。仿真结果表明,此评估模型具有较高的准确性和较快的计算效率。 TP18; 针对高校科研能力评估过程中存在的多因素、高非线性特点,经典评估模型主观性较强,导致模型评估准确性不高的问题,提出基于协同智能水滴算法IWD和粗糙集块神经网络RBNN的高校科研能力评估模型.首先,引入智能水滴算法,并针对传统智能水滴算法固定旁域搜索范围不利于提升算法搜索效率的问题,提出一种局部空间自动缩放算法LSAS,该算法根据当前种群最优个体,自动调整下一步搜索空间大小,对进化过程进行指导,提高算法的进化效率;其次,基于粗糙集理论对高校科研能力数据进行特征预处理,简化数据计算量;最后,对块神经网络和粗糙集参数进行编码,并对高校科研能力模型进行评估.仿真结果表明,此评估模型具有较高的准确性和较快的计算效率. |
| Author | 刘春霞 田芸 |
| AuthorAffiliation | 滨州学院信息工程系,山东滨州256603 首都师范大学数学科学学院,北京100048 |
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| Author_FL | TIAN Yun LIU Chun-xia |
| Author_FL_xml | – sequence: 1 fullname: LIU Chun-xia – sequence: 2 fullname: TIAN Yun |
| Author_xml | – sequence: 1 fullname: 刘春霞 田芸 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitleAlternate | An evaluation model of scientific research ability of universities based on cooperative IWD and RBNN |
| DocumentTitle_FL | An evaluation model of scientific research ability of universities based on cooperative IWD and RBNN |
| EndPage | 493 |
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| Keywords | 智能水滴 cooperative computation 粗糙集 协同计算 rough set efficient scientific research ability 高校科研能力 block neural network 块神经网络 intelligent water drop (IWD) |
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| Notes | intelligent water drop（IWD）; block neural network; rough set; efficient scientific research ability; cooperative computation Aiming at the problems in the evaluation process of the scientific research abilities due to its multiple factors and high non linear characteristics,as well as the subjective assessment in classic models,which leads to low accuracy,we propose an evaluation model of the collaborative ability of scientific research based on the intelligent water drops（IWD）algorithm and the rough set block neural network（RBNN）.We first introduce the IWD algorithm,and design a local spatial auto scaling algorithm（LSSA）to solve the problem of fixed beside domain search range of the traditional IWD algorithm that is not conducive to improve search efficiency.The LSSA can automatically adjust the next search space size according to the best individual of current population,thus improving the evolutionary efficiency of algorithms.Based on the rough set theory,the data of scientific research ability of universit |
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| Publisher | 首都师范大学数学科学学院,北京100048%首都师范大学数学科学学院,北京,100048 滨州学院信息工程系,山东滨州256603 |
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| SubjectTerms | 协同计算 块神经网络 智能水滴 粗糙集 高校科研能力 |
| Title | 高校科研能力的协同IWD粗糙集-块神经网络评估模型 |
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