发布求助
文献互助 智能选刊 最新文献

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)最新文献

筛选
英文 中文
Title pages 标题页
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2018-07-16 DOI: 10.14220/9783737008372.front
H. Prabha
{"title":"Title pages","authors":"H. Prabha","doi":"10.14220/9783737008372.front","DOIUrl":"https://doi.org/10.14220/9783737008372.front","url":null,"abstract":"for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"246 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126819637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Solving sudoku with Membrane Computing 用膜计算解决数独
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645195
D. Díaz-Pernil, Carlos M. Fernández-Márquez, Manuel García-Quismondo, M. A. Gutiérrez-Naranjo, Miguel A. Martínez-del-Amor
{"title":"Solving sudoku with Membrane Computing","authors":"D. Díaz-Pernil, Carlos M. Fernández-Márquez, Manuel García-Quismondo, M. A. Gutiérrez-Naranjo, Miguel A. Martínez-del-Amor","doi":"10.1109/BICTA.2010.5645195","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645195","url":null,"abstract":"Sudoku is a very popular puzzle which consists on placing several numbers in a squared grid according to some simple rules. In this paper we present an efficient family of P systems which solve sudokus of any order verifying a specific property. The solution is searched by using a simple human-style method. If the sudoku cannot be solved by using this strategy, the P system detects this drawback and then the computations stops and returns No. Otherwise, the P system encodes the solution and returns Yes in the last computation step.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115597560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Remote sensing images classification using fuzzy-rough neural network 基于模糊粗糙神经网络的遥感图像分类
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645221
Mao Jianxu, Liu Caiping, W. Yao-nan
{"title":"Remote sensing images classification using fuzzy-rough neural network","authors":"Mao Jianxu, Liu Caiping, W. Yao-nan","doi":"10.1109/BICTA.2010.5645221","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645221","url":null,"abstract":"In remote sensing images classification, the boundaries between different classes are vague and it is often difficult or impossible to acquire all of the necessary essential features for precisely classification. So both the fuzzy uncertainty and rough uncertainty are presented. Based on fuzzy-rough set theory, a fuzzy-rough neural network (FRNN) is designed for remote sensing images classification. In the FRNN classification algorithm, fuzzy set, rough set and neural network technique are combined. Fuzzy-rough function is used as membership function of the FRNN and integrates the ability of processing fuzzy and rough uncertainty information, which endue the FRNN classifier with better capability of learning and self-adapt. Experimental results show that the proposed classification algorithm can be used in remote sensing images classification, and its classification precision is superior to that of the conventional maximum likelihood algorithm and radial basis function neural network (RBFNN) algorithm.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121020926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Nonlinear PSO—Convergence analysis and parameter adjustment schemes 非线性pso收敛分析及参数调整方案
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645105
Xuexue Zhao, Gang-lin Wang
{"title":"Nonlinear PSO—Convergence analysis and parameter adjustment schemes","authors":"Xuexue Zhao, Gang-lin Wang","doi":"10.1109/BICTA.2010.5645105","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645105","url":null,"abstract":"Linear parameter adjustment (LP A) schemes had been widely used in particle swarm optimization (PSO). In this paper, we develop a novel PSO algorithm with nonlinear parameter adjustment (NLP A) called nonlinear PSO and present its convergence analysis. Simulations on five standard test functions confirm the validity of the nonlinear parameter adjustment methods.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121031511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Non-fragile H∞ control for a class of nonlinear sampled-data system 一类非线性采样数据系统的非脆弱H∞控制
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645117
Shigang Wang, Zhiqiang Hu, Yingsong Li
{"title":"Non-fragile H∞ control for a class of nonlinear sampled-data system","authors":"Shigang Wang, Zhiqiang Hu, Yingsong Li","doi":"10.1109/BICTA.2010.5645117","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645117","url":null,"abstract":"This paper considers the non-fragile control problem for uncertain nonlinear sampled-data system and controller gain perturbations. Firstly, the continuous control plant of sampled-data system is transformed into a discrete system model with nonlinear. Then, the Lyapunov stability theory and the linear matrix inequality (LMI) approach are applied to design a non-fragile controller, which results in the closed-loop system being asymptotically stable and the system's performance index being less than a given value. At the same time, the existence condition and the design approach of a non-fragile controller are presented. Finally, simulation examples are employed to verify the validity of the proposed control algorithm.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121108922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Optimization on GA-BP neural network of coal and gas outburst hazard prediction 煤与瓦斯突出危险性预测GA-BP神经网络优化
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645206
Bo Wu, Shiyue Wu, Xiaofeng Liu
{"title":"Optimization on GA-BP neural network of coal and gas outburst hazard prediction","authors":"Bo Wu, Shiyue Wu, Xiaofeng Liu","doi":"10.1109/BICTA.2010.5645206","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645206","url":null,"abstract":"This paper presents a genetic algorithm and back propagation neural network (GA-BP-NN) outburst prediction model with a structure of 6 × 13 × 1 according to basic theory of coal and gas outburst hazard classification prediction of coal mine and genetic algorithm, back propagation and neural network. Particularly, we also construct an application of outburst prediction of coal mine. From the learning of living examples of an area in Shanxi province in China, we could safely draw the conclusions as followed: a proper number of learning samples is 12∼18 when there are 6 input neurons of outburst prediction; In addition, the network generalization capability could be enhanced by increasing number of classes which belong to the training samples and decreasing distances of sample intervals; When the Logsig delivery function is taken in output layer, the pattern classification of network is best and the critical value of outburst prediction criterion is 0.5; When the pattern classification of network is best, other parameters have little influence on the network capability. The application and conclusions could be taken in Prediction of Coal and Gas Outburst of coal mining and contribute greatly to production safety of coal mine.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127146251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
A stability analysis of a quadratic function chemostat model with time delay 具有时滞的二次函数恒化模型的稳定性分析
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645113
Daijun Wei, Dalei Wang
{"title":"A stability analysis of a quadratic function chemostat model with time delay","authors":"Daijun Wei, Dalei Wang","doi":"10.1109/BICTA.2010.5645113","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645113","url":null,"abstract":"Based on some biological meanings, a class of quadratic function model with time delay is considered. A detailed analysis on existence and bounds of its solutions and local asymptotic stability of its equilibrium is carried out.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124804009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An animal disease diagnosis system based on the architecture of binary-inference-core 基于二元推理核结构的动物疾病诊断系统
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645236
Wenxue Tan, Xiping Wang, Jinju Xi
{"title":"An animal disease diagnosis system based on the architecture of binary-inference-core","authors":"Wenxue Tan, Xiping Wang, Jinju Xi","doi":"10.1109/BICTA.2010.5645236","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645236","url":null,"abstract":"in this paper, we propose a binary-inference-core diagnosis mechanism, which based on the two algorithms: one named Weighted Uncertainty Reason Algorithm Supporting Certainty Factor Speculation and another named Improved Bayesian method supporting machine learning. On the basis of that, its corresponding software system prototype is constructed, and some novel terms and algorithms are initiated systematically. Experimental statistics show that in contrast to the AI diagnosis system based on the traditional mono-inference-core, the binary-inference-core system is able to significantly improve inference accuracy and utilization rate of field knowledge, and its accurate rate is over 92%, while it provides contrast of results from different algorithm, presenting an agreeable macro effect of diagnosis.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122839219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Timed spiking neural P systems 定时脉冲神经系统
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645192
Hong Peng, Jun Wang, Gexiang Zhang, M. Gheorghe
{"title":"Timed spiking neural P systems","authors":"Hong Peng, Jun Wang, Gexiang Zhang, M. Gheorghe","doi":"10.1109/BICTA.2010.5645192","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645192","url":null,"abstract":"In this paper, we present a new class of spiking neural P systems for handling temporal information and representing temporal knowledge, called timed spiking neural P systems. A new firing principle is introduced into the timed spiking neural P systems instead of original firing and delay mechanisms in spiking neural P systems. The timed spiking neural P systems can effectively represent both qualitative and quantitative temporal information.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114183569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Knowledge representation using fuzzy spiking neural P system 基于模糊脉冲神经P系统的知识表示
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645191
Tao Wang, Jun Wang, Hong Peng, Yanli Deng
{"title":"Knowledge representation using fuzzy spiking neural P system","authors":"Tao Wang, Jun Wang, Hong Peng, Yanli Deng","doi":"10.1109/BICTA.2010.5645191","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645191","url":null,"abstract":"This paper presents a fuzzy spiking neural P system (FSN P system) to represent the fuzzy production rules in a knowledge base of a rule-based system, where the certainty factors of fuzzy production rules and the truth values of propositions are described by trapezoidal fuzzy numbers. In the proposed FSN P system, the definition of traditional neurons has been extended. The neurons are divided into two types: proposition neurons and rule neurons; the content of each neuron is a trapezoidal fuzzy number in [0, 1] instead of an integer. Also the fuzzy reasoning process can be modeled by the proposed FSN P system.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121896615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信