Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226839
P. Miller, A. Inoue
This paper presents an intrusion detection system consisting of multiple intelligent agents. Each agent uses a self-organizing map (SOM) in order to detect intrusive activities on a computer network. A blackboard mechanism is used for the aggregation of results generated from such agents (i.e. a group decision). In addition, this system is capable of reinforcement learning with the reinforcement signal generated within the blackboard and then distributed over all agents which are involved in the group decision making. Systems with various configurations of agents are evaluated for criteria such as speed, accuracy, and consistency. The results indicate an increase in classification accuracy as well as in its constancy as more sensors are incorporated. Currently this system is primarily tested on the data set for KDD Cup '99.
提出了一种由多个智能代理组成的入侵检测系统。每个代理使用自组织映射(SOM)来检测计算机网络上的侵入性活动。黑板机制用于聚合这些代理生成的结果(即群体决策)。此外，该系统能够利用黑板内部产生的强化信号进行强化学习，然后将强化信号分布到参与群体决策的所有智能体上。具有各种代理配置的系统将根据速度、准确性和一致性等标准进行评估。结果表明，随着传感器数量的增加，分类精度和稳定性都有所提高。目前，该系统主要在KDD Cup '99的数据集上进行测试。
{"title":"Collaborative intrusion detection system","authors":"P. Miller, A. Inoue","doi":"10.1109/NAFIPS.2003.1226839","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226839","url":null,"abstract":"This paper presents an intrusion detection system consisting of multiple intelligent agents. Each agent uses a self-organizing map (SOM) in order to detect intrusive activities on a computer network. A blackboard mechanism is used for the aggregation of results generated from such agents (i.e. a group decision). In addition, this system is capable of reinforcement learning with the reinforcement signal generated within the blackboard and then distributed over all agents which are involved in the group decision making. Systems with various configurations of agents are evaluated for criteria such as speed, accuracy, and consistency. The results indicate an increase in classification accuracy as well as in its constancy as more sensors are incorporated. Currently this system is primarily tested on the data set for KDD Cup '99.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122481513","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}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226786
Hanji Shang, Yuchu Lu, Ping Jin
This paper presents an application of the information diffusion method to the small sample problems, which appeared in the investigation of coronary heart disease. The information diffusion method is applied to a higher dimensional case and a limited departure criterion is introduced to improve the optimization model for the selection of diffusion function.
{"title":"Higher dimensional information diffusion and its application","authors":"Hanji Shang, Yuchu Lu, Ping Jin","doi":"10.1109/NAFIPS.2003.1226786","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226786","url":null,"abstract":"This paper presents an application of the information diffusion method to the small sample problems, which appeared in the investigation of coronary heart disease. The information diffusion method is applied to a higher dimensional case and a limited departure criterion is introduced to improve the optimization model for the selection of diffusion function.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131647994","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}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226773
Y. Hata, O. Ishikawa, Syoji Kobashi, K. Kondo
This paper defines normality in human body for diagnostic analysis of signs observed in human body. The normality is a matter of degree. Physician usually examines whether a patient is either normal or abnormal. Diagnosis of human body is usually done by observing biosignals, radiological images, body surface information and others of human body. First, the information granularity of these signs of human body is shown. The normality is defined in the theory of hierarchical definability. According to the definition, a calculation method of the degree of normality is introduced. Finally, the examples of the degree of normality are shown.
{"title":"Degree of normality based on fuzzy logic for a diagnostic analysis of signs observed in a human body","authors":"Y. Hata, O. Ishikawa, Syoji Kobashi, K. Kondo","doi":"10.1109/NAFIPS.2003.1226773","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226773","url":null,"abstract":"This paper defines normality in human body for diagnostic analysis of signs observed in human body. The normality is a matter of degree. Physician usually examines whether a patient is either normal or abnormal. Diagnosis of human body is usually done by observing biosignals, radiological images, body surface information and others of human body. First, the information granularity of these signs of human body is shown. The normality is defined in the theory of hierarchical definability. According to the definition, a calculation method of the degree of normality is introduced. Finally, the examples of the degree of normality are shown.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122066991","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}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226756
M. Akbarzadeh-T., I. Mosavat, S. Abbasi
A novel approach is proposed to combine the strengths of GA and GP to optimize rule sets and membership functions of fuzzy systems in a co-evolutionary strategy in order to avoid the problem of dual representation in fuzzy systems. The novelty of proposed algorithm is twofold. One is that GP is used for the structural part (Rule sets) and GA for the string part (Membership functions). The goal is to reduce/eliminate the problem of competing conventions by co-evolving pieces of the problem separately and then in combination. Second is exploiting the synergism between rules sets and membership functions by imitating the effect of "matching" and friendship in cooperating teams of humans, thereby significantly reducing the number of function evaluations necessary for evolution. The method is applied to a chaotic time series prediction problem and compared with the standard fuzzy table look-up scheme. demonstrate several significant improvements with the proposed approach; specifically, four times higher fitness and more steady fitness improvements as compared with epochal improvements observed in GP.
{"title":"Friendship modeling for cooperative co-evolutionary fuzzy systems: a hybrid GA-GP algorithm","authors":"M. Akbarzadeh-T., I. Mosavat, S. Abbasi","doi":"10.1109/NAFIPS.2003.1226756","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226756","url":null,"abstract":"A novel approach is proposed to combine the strengths of GA and GP to optimize rule sets and membership functions of fuzzy systems in a co-evolutionary strategy in order to avoid the problem of dual representation in fuzzy systems. The novelty of proposed algorithm is twofold. One is that GP is used for the structural part (Rule sets) and GA for the string part (Membership functions). The goal is to reduce/eliminate the problem of competing conventions by co-evolving pieces of the problem separately and then in combination. Second is exploiting the synergism between rules sets and membership functions by imitating the effect of \"matching\" and friendship in cooperating teams of humans, thereby significantly reducing the number of function evaluations necessary for evolution. The method is applied to a chaotic time series prediction problem and compared with the standard fuzzy table look-up scheme. demonstrate several significant improvements with the proposed approach; specifically, four times higher fitness and more steady fitness improvements as compared with epochal improvements observed in GP.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129882486","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}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226843
Arun Khosla, Sandeep Kumar, K. K. Aggarwal
ANFIS architecture is a class of adaptive networks, which is functionally equivalent to fuzzy inference systems. The architecture has been employed for fuzzy modeling that learns information about a data-set in order to compute the membership functions and rule-base that best follow the given input-output data. ANFIS employs hybrid learning that combines the gradient method and the least squares estimates to identify premise and consequent parameters respectively. In this paper the fuzzy controller for rapidly charging nickel-cadmium (Ni-Cd) batteries charger has been designed through ANFIS. The behavior of Ni-Cd batteries was not known for higher charging rates and the input-output data of batteries has been obtained through rigorous experimentation with an objective to charge the batteries as quickly as possible, but without doing any damage to them. Takagi-Sugeno-Kang (TSK) model has been considered for the controller.
{"title":"Fuzzy controller for rapid nickel-cadmium batteries charger through adaptive neuro-fuzzy inference system (ANFIS) architecture","authors":"Arun Khosla, Sandeep Kumar, K. K. Aggarwal","doi":"10.1109/NAFIPS.2003.1226843","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226843","url":null,"abstract":"ANFIS architecture is a class of adaptive networks, which is functionally equivalent to fuzzy inference systems. The architecture has been employed for fuzzy modeling that learns information about a data-set in order to compute the membership functions and rule-base that best follow the given input-output data. ANFIS employs hybrid learning that combines the gradient method and the least squares estimates to identify premise and consequent parameters respectively. In this paper the fuzzy controller for rapidly charging nickel-cadmium (Ni-Cd) batteries charger has been designed through ANFIS. The behavior of Ni-Cd batteries was not known for higher charging rates and the input-output data of batteries has been obtained through rigorous experimentation with an objective to charge the batteries as quickly as possible, but without doing any damage to them. Takagi-Sugeno-Kang (TSK) model has been considered for the controller.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125572200","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}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226788
M. Alexiuk, N. Pizzi
Functional magnetic resonance imaging (FMRI) datasets are composed of spatial and temporal features and contain unique noise degradation. We propose a feature partition along noise-specific domains to fit the fuzzy c-means (FCM) algorithm to this problem. Each domain will consist of unique features and use a domain-specific metric. The distance term in the FCM membership update equation is replaced by a weighted sum of domain distances. Exploiting conceptual cleavage of the sample features invites intuitive remedial action in the form of robust metrics, decreased weighting, or selective enhancement processing. Robust centroids are determined by suppressing the role of feature subsets contaminated by significant noise levels or intractable noise types. This paper examines synthetic datasets of FMRI activations and shows that a specialized FCM algorithm determines higher accuracy centroids in the presence of high noise levels.
{"title":"Robust centroid determination of noisy data using FCM and domain specific partitioning","authors":"M. Alexiuk, N. Pizzi","doi":"10.1109/NAFIPS.2003.1226788","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226788","url":null,"abstract":"Functional magnetic resonance imaging (FMRI) datasets are composed of spatial and temporal features and contain unique noise degradation. We propose a feature partition along noise-specific domains to fit the fuzzy c-means (FCM) algorithm to this problem. Each domain will consist of unique features and use a domain-specific metric. The distance term in the FCM membership update equation is replaced by a weighted sum of domain distances. Exploiting conceptual cleavage of the sample features invites intuitive remedial action in the form of robust metrics, decreased weighting, or selective enhancement processing. Robust centroids are determined by suppressing the role of feature subsets contaminated by significant noise levels or intractable noise types. This paper examines synthetic datasets of FMRI activations and shows that a specialized FCM algorithm determines higher accuracy centroids in the presence of high noise levels.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120964052","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}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226824
Jonathan, T. Ross
In an attempt to understand the nonlinear and poorly conditioned phenomena of the response of rigid structures subject to ground motion or vibrating (rocking) freely, these structures are idealized as rigid blocks. Despite this idealization, the problem of simulating the response of rigid blocks is still a very difficult problem in solid mechanics. This work represents a new paradigm to simulate this complex problem, by using linguistic descriptions of the system behavior within a fuzzy systems environment. This work also addresses computational efficiencies using rule-base reduction. Two methods for rule-base reduction are implemented, Singular Value Decomposition and Combs Method for Rapid Inference. These methods have been previously shown to be effective for reducing rule-base size. The two methods are compared on the same physical system. This comparison elucidates their accuracy and limitations.
{"title":"Rocking rigid blocks simulations using fuzzy systems theory with rule reduction","authors":"Jonathan, T. Ross","doi":"10.1109/NAFIPS.2003.1226824","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226824","url":null,"abstract":"In an attempt to understand the nonlinear and poorly conditioned phenomena of the response of rigid structures subject to ground motion or vibrating (rocking) freely, these structures are idealized as rigid blocks. Despite this idealization, the problem of simulating the response of rigid blocks is still a very difficult problem in solid mechanics. This work represents a new paradigm to simulate this complex problem, by using linguistic descriptions of the system behavior within a fuzzy systems environment. This work also addresses computational efficiencies using rule-base reduction. Two methods for rule-base reduction are implemented, Singular Value Decomposition and Combs Method for Rapid Inference. These methods have been previously shown to be effective for reducing rule-base size. The two methods are compared on the same physical system. This comparison elucidates their accuracy and limitations.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"79 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125889487","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}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226814
F. Karbou
In this paper, we propose an extension of the principle of the interval's approach to define new directional and qualitative topological relations where the description is more precise and where an adaptation of the concept of earlier methods based on Ellen's algebra is possible. Thus, for any reference position we want to locate (beginning, middle, between two sets, under, ...), we had only to locate the position by the reference sets and then define the desired spatial relationships by comparing the percentage of the interior parts/exterior parts regarding this reference.
{"title":"On interval descriptions of spatial relations","authors":"F. Karbou","doi":"10.1109/NAFIPS.2003.1226814","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226814","url":null,"abstract":"In this paper, we propose an extension of the principle of the interval's approach to define new directional and qualitative topological relations where the description is more precise and where an adaptation of the concept of earlier methods based on Ellen's algebra is possible. Thus, for any reference position we want to locate (beginning, middle, between two sets, under, ...), we had only to locate the position by the reference sets and then define the desired spatial relationships by comparing the percentage of the interior parts/exterior parts regarding this reference.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131190054","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}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226764
W. Siler
The important idea of determining "Approximate X", in which X can be almost anything, was put forward by Lotfi Zadeh early last year. Implementing programs to realize this powerful concept involves abandoning some cherished ideas, and adopting some new ones. Zadeh's famous 1965 fuzzy set paper laid out the basis for Approximate X; the discrete fuzzy set, whose members are words. However, from the beginning there was a concentration on words that describe numbers; the concepts of linguistic variable and membership function defined on the real line obscured the more general case, in which the members of a discrete fuzzy set are words that can represent almost anything. The development of typical fuzzy control rules, with inescapable fuzzification of input numbers and defuzzification into output numbers, pushed non-numeric fuzzy sets further into the background. In this paper we take up in some detail the nature of programs designed to produce output in words rather than numbers: appropriate data types, inference methods, rule-firing patterns and definitions of possibility and necessity.
{"title":"Implementing Approximate X","authors":"W. Siler","doi":"10.1109/NAFIPS.2003.1226764","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226764","url":null,"abstract":"The important idea of determining \"Approximate X\", in which X can be almost anything, was put forward by Lotfi Zadeh early last year. Implementing programs to realize this powerful concept involves abandoning some cherished ideas, and adopting some new ones. Zadeh's famous 1965 fuzzy set paper laid out the basis for Approximate X; the discrete fuzzy set, whose members are words. However, from the beginning there was a concentration on words that describe numbers; the concepts of linguistic variable and membership function defined on the real line obscured the more general case, in which the members of a discrete fuzzy set are words that can represent almost anything. The development of typical fuzzy control rules, with inescapable fuzzification of input numbers and defuzzification into output numbers, pushed non-numeric fuzzy sets further into the background. In this paper we take up in some detail the nature of programs designed to produce output in words rather than numbers: appropriate data types, inference methods, rule-firing patterns and definitions of possibility and necessity.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134146831","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}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226792
Hu Qing, H. Juan
Fuzzy ideals were discussed in BCK/BCI-algebras, but in BCH-algebras. In this paper we introduce notions of fuzzy ideals in BCH-algebras and discuss their properties in BCH-algebras.
{"title":"Fuzzy ideals in BCH-algebras","authors":"Hu Qing, H. Juan","doi":"10.1109/NAFIPS.2003.1226792","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226792","url":null,"abstract":"Fuzzy ideals were discussed in BCK/BCI-algebras, but in BCH-algebras. In this paper we introduce notions of fuzzy ideals in BCH-algebras and discuss their properties in BCH-algebras.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132265821","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}
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