发布求助
文献互助 智能选刊 最新文献

SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)最新文献

筛选
英文 中文
New results in breast cancer classification obtained from an evolutionary computation/adaptive boosting hybrid using mammogram and history data 利用乳房x线照片和历史数据，从进化计算/自适应增强混合方法中获得乳腺癌分类的新结果
SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504) Pub Date : 2001-06-25 DOI: 10.1109/SMCIA.2001.936727
W. Land, T. Masters, J. Lo, D.W. McKee, F. R. Anderson
{"title":"New results in breast cancer classification obtained from an evolutionary computation/adaptive boosting hybrid using mammogram and history data","authors":"W. Land, T. Masters, J. Lo, D.W. McKee, F. R. Anderson","doi":"10.1109/SMCIA.2001.936727","DOIUrl":"https://doi.org/10.1109/SMCIA.2001.936727","url":null,"abstract":"A new neural network technology was developed to improve the diagnosis of breast cancer using mammogram findings. The paradigm, adaptive boosting (AB), uses a markedly different theory in solving the computational intelligence (CI) problem. AB, a new machine learning paradigm, focuses on finding weak learning algorithm(s) that initially need to provide slightly better than \"random\" performance (i.e., approximately 55%) when processing a mammogram training set. By successive development of additional architectures (using the mammogram training set), the adaptive boosting process improves performance of the basic evolutionary programming derived neural network architectures. The results of these several EP-derived hybrid architectures are then intelligently combined and tested using a similar validation mammogram data set. Optimization, focused on improving specificity and positive predictive value at very high sensitivities, with an analysis of the performance of the hybrid would be most meaningful. Using the DUKE mammogram database of 500 biopsy proven samples, this hybrid, on average, was able to achieve (under statistical 5-fold cross-validation) a specificity of 48.3% and a positive predictive value (PPV) of 51.8% while maintaining 100% sensitivity. At 97% sensitivity, a specificity of 56.6% and a PPV of 55.8% were obtained.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133147047","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: 20
Intelligent hybrid load forecasting system for an electric power company 某电力公司智能混合负荷预测系统
SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504) Pub Date : 2001-06-25 DOI: 10.1109/SMCIA.2001.936723
H. Lewis
{"title":"Intelligent hybrid load forecasting system for an electric power company","authors":"H. Lewis","doi":"10.1109/SMCIA.2001.936723","DOIUrl":"https://doi.org/10.1109/SMCIA.2001.936723","url":null,"abstract":"The paper presents a system for day-ahead load forecasting as originally proposed to a regional electric power company. The company provided funding for developing most parts of this software. The system is based on a hybrid approach to intelligent systems design combining a fuzzy heuristic approach based on the knowledge of human experts in load forecasting with a data-driven neural network-based component. To make the system truly useful, considerable emphasis was placed on the user interface including a highly developed explanation module.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"84 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131472333","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: 9
DFSLIF: dynamical fuzzy system with linguistic information feedback 具有语言信息反馈的动态模糊系统
SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504) Pub Date : 2001-06-25 DOI: 10.1109/SMCIA.2001.936744
X.Z. Gao, S. Ovaska, Y. Dote
{"title":"DFSLIF: dynamical fuzzy system with linguistic information feedback","authors":"X.Z. Gao, S. Ovaska, Y. Dote","doi":"10.1109/SMCIA.2001.936744","DOIUrl":"https://doi.org/10.1109/SMCIA.2001.936744","url":null,"abstract":"We propose a new dynamical fuzzy system with linguistic information feedback (DFSLIF). Instead of crisp system output, the delayed conclusion fuzzy membership function in the consequence part is fed back locally with adjustable scaling and shifting in order to overcome the static mapping drawback of conventional fuzzy systems. We give a detailed description of the corresponding structure and algorithm. Our novel scheme has the advantage of inherent dynamics, and is therefore well suited for handling temporal problems like dynamical system identification, control, and filtering. Simulation experiments have been carried out to demonstrate its effectiveness.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131748625","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
Feature selection for in-silico drug design using genetic algorithms and neural networks 基于遗传算法和神经网络的芯片药物设计特征选择
SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504) Pub Date : 2001-06-25 DOI: 10.1109/SMCIA.2001.936728
M. Ozdemir, M. Embrechts, F. Arciniegas, C. Breneman, L. Lockwood, Kristin P. Bennett
{"title":"Feature selection for in-silico drug design using genetic algorithms and neural networks","authors":"M. Ozdemir, M. Embrechts, F. Arciniegas, C. Breneman, L. Lockwood, Kristin P. Bennett","doi":"10.1109/SMCIA.2001.936728","DOIUrl":"https://doi.org/10.1109/SMCIA.2001.936728","url":null,"abstract":"QSAR (quantitative structure activity relationship) is a discipline within computational chemistry that deals with predictive modeling, often for relatively small datasets where the number of features might exceed the number of data points, leading to extreme dimensionality problems. The paper addresses a novel feature selection procedure for QSAR based on genetic algorithms to reduce the curse of dimensionality problem. In this case the genetic algorithm minimizes a cost function derived from the correlation matrix between the features and the activity of interest that is being modeled. From a QSAR dataset with 160 features, the genetic algorithm selected a feature subset (40 features), which built a better predictive model than with full feature set. The results for feature reduction with genetic algorithm were also compared with neural network sensitivity analysis.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130866264","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: 34
Neural network training for complex industrial applications 复杂工业应用的神经网络训练
SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504) Pub Date : 2001-06-25 DOI: 10.1109/SMCIA.2001.936720
H. Vanlandingham, F. Azam, W. Pulliam
{"title":"Neural network training for complex industrial applications","authors":"H. Vanlandingham, F. Azam, W. Pulliam","doi":"10.1109/SMCIA.2001.936720","DOIUrl":"https://doi.org/10.1109/SMCIA.2001.936720","url":null,"abstract":"The paper presents two methods of training multilayer perceptrons (MLPs) that use both functional values and co-located derivative values during the training process. The first method extends the standard backpropagation training algorithm for MLPs whereas the second method employs genetic algorithms (GAs) to find the optimal neural network weights using both functional and co-located function derivative values. The GAs used for optimization of the weights of a feedforward artificial neural network use a special reordering of the genotype before recombination. The ultimate goal of this research effort is to be able to train and design an artificial neural networks (ANN) more effectively, i.e., to have a network that generalizes better, learns faster and requires fewer training data points. The initial results indicate that the methods do, in fact, provide good generalization while requiring only a relatively sparse sampling of the function and its derivative values during the training phase, as indicated by the illustrative examples.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128313073","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
Scientific data mining with StripMiner/sup TM/ 科学数据挖掘与StripMiner/sup TM/
SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504) Pub Date : 2001-06-25 DOI: 10.1109/SMCIA.2001.936721
M. Embrechts, F. Arciniegas, M. Ozdemir, M. Momma
{"title":"Scientific data mining with StripMiner/sup TM/","authors":"M. Embrechts, F. Arciniegas, M. Ozdemir, M. Momma","doi":"10.1109/SMCIA.2001.936721","DOIUrl":"https://doi.org/10.1109/SMCIA.2001.936721","url":null,"abstract":"The paper introduces scientific data mining, the standard data-mining problem, and the strip-mining problem. StripMiner/sup TM/, a shell program for feature reduction and predictive modeling, integrates the executions of several different machine-learning models (partial least squares regression, genetic algorithms, support vector machines, neural networks, and local learning). This paper introduces the StripMiner/sup TM/ code, its functionality, and its options.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114988908","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
Advancing the human experience with interactive evolutionary computation 用交互式进化计算推进人类体验
SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504) Pub Date : 2001-06-25 DOI: 10.1109/SMCIA.2001.936746
H. Takagi
{"title":"Advancing the human experience with interactive evolutionary computation","authors":"H. Takagi","doi":"10.1109/SMCIA.2001.936746","DOIUrl":"https://doi.org/10.1109/SMCIA.2001.936746","url":null,"abstract":"We first overview the research trend of computational intelligence, discuss what comes next in the computational intelligence research, and conclude that humanized technologies would be one of the essential keywords of the possible research direction. Then, we take up interactive evolutionary computation (IEC) as one of the humanized technologies and show how IEC technology has spread to a wide variety of fields, what problems remain, and what kinds of challenges need to be solved, and how to make the technology practical.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122621432","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
Approximate reasoning algorithm for short term aircraft assignment 短期飞机分配的近似推理算法
SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504) Pub Date : 2001-06-25 DOI: 10.1109/SMCIA.2001.936740
D. Teodorovic, P. Lucic
{"title":"Approximate reasoning algorithm for short term aircraft assignment","authors":"D. Teodorovic, P. Lucic","doi":"10.1109/SMCIA.2001.936740","DOIUrl":"https://doi.org/10.1109/SMCIA.2001.936740","url":null,"abstract":"The problem considered in this paper is as follows: assign the available aircraft from a fleet to specific routes so that the aircraft are kept in normal operation as long as possible before going to the technical base, taking care that \"higher quality\" aircraft are assigned to \"more important\" routes. This paper develops a model for aircraft assignment that includes both numerical and linguistic information normally used by dispatchers. The developed model is tested on a real numerical example.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129061499","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
Remote control of industrial processes 工业过程的远程控制
SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504) Pub Date : 2001-06-25 DOI: 10.1109/SMCIA.2001.936736
P. Dadone, H. Vanlandingham
{"title":"Remote control of industrial processes","authors":"P. Dadone, H. Vanlandingham","doi":"10.1109/SMCIA.2001.936736","DOIUrl":"https://doi.org/10.1109/SMCIA.2001.936736","url":null,"abstract":"This paper proposes and describes an application of remote control for an industrial process. A control program implemented on a PC using the Java language allows for easy prototyping of a fuzzy logic controller. The PC is connected through a data acquisition card to a laboratory process (used in a control teaching laboratory). The fuzzy controller is easily setup and adjusted, allowing for the control of the process. The Java implementation conceptually permits a portable and remote measurement and control approach for any industrial process.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129079170","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: 1
Adaptive power plant start-up scheduling: simulation test results 自适应发电厂启动调度：模拟测试结果
SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504) Pub Date : 2001-06-25 DOI: 10.1109/SMCIA.2001.936724
A. Kamiya, K. Kawai, I. Ono, S. Kobayashi
{"title":"Adaptive power plant start-up scheduling: simulation test results","authors":"A. Kamiya, K. Kawai, I. Ono, S. Kobayashi","doi":"10.1109/SMCIA.2001.936724","DOIUrl":"https://doi.org/10.1109/SMCIA.2001.936724","url":null,"abstract":"Power plant start-up scheduling is aimed at minimizing the start-up time while limiting maximum turbine-rotor stresses. A shorter start-up time not only reduces fuel and electricity consumption during the start-up process, but also increases its capability of adapting to changes in electricity demand. This scheduling problem is, however, highly nonlinear with a number of local optima within a wide search space. In our previous research, we proved that the optimal schedule stays on the edge of the feasible space, and provided an adaptive enforcement operation based on a theoretical setting equation. The adaptive enforcement operation used with GA is applied to compel the search along the edge of the feasible space, so as to increase the search efficiency. We give a brief description of the theoretical proof and present simulation test results with a range of hard-to-search stress limit sets to verify the search efficiency of the theoretically-proved search model.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132057669","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
首页 上一页 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