An intelligent optimization approach is proposed for eigenstructure assignment (EA) via neural network (NN) adjusting the components of output vector autonomously. The Basic idea is to minimize the L2 norm of error between the desired vector and achievable vector using the designing freedom provided by EA technique. Besides adjusting the output vector parameters, the closed-loop eigenvalues are also optimized within desired regions on the left-half complex plane to ensure both closed-loop stability and dynamical performance. With the proposed approach, additional closed-loop specifications such as decoupling of different modes and robustness can also be easily achieved. As a demonstration, application of the proposed approach to the designing of flight control law for an advanced fighter is discussed. The results show good closed loop performance and validate the proposed intelligent optimization approach of EA technique
{"title":"Intelligent Optimization Approach of Eigenstructure Assignment Based Flight Control for Advanced Fighter","authors":"Yong Fan, Jihong Zhu, Zeng-qi Sun","doi":"10.1109/SYNASC.2006.48","DOIUrl":"https://doi.org/10.1109/SYNASC.2006.48","url":null,"abstract":"An intelligent optimization approach is proposed for eigenstructure assignment (EA) via neural network (NN) adjusting the components of output vector autonomously. The Basic idea is to minimize the L2 norm of error between the desired vector and achievable vector using the designing freedom provided by EA technique. Besides adjusting the output vector parameters, the closed-loop eigenvalues are also optimized within desired regions on the left-half complex plane to ensure both closed-loop stability and dynamical performance. With the proposed approach, additional closed-loop specifications such as decoupling of different modes and robustness can also be easily achieved. As a demonstration, application of the proposed approach to the designing of flight control law for an advanced fighter is discussed. The results show good closed loop performance and validate the proposed intelligent optimization approach of EA technique","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"151 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131019313","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}
The target of workflow mining is to obtain a workflow management systems from a possibly complete set of event logs of the system. This gives a fruitful support for developing complex business transaction sequences and business collaboration applications. We investigate on how the theory of regions, used to synthesize nets from marking graphs, can be used to mine workflow nets. We show that using minimal regions we can mine the correct net. We also briefly discuss on how transitions systems can be obtained by event logs
{"title":"Characterizing Workflow Nets Using Regions","authors":"N. Busi, G. Pinna","doi":"10.1109/SYNASC.2006.21","DOIUrl":"https://doi.org/10.1109/SYNASC.2006.21","url":null,"abstract":"The target of workflow mining is to obtain a workflow management systems from a possibly complete set of event logs of the system. This gives a fruitful support for developing complex business transaction sequences and business collaboration applications. We investigate on how the theory of regions, used to synthesize nets from marking graphs, can be used to mine workflow nets. We show that using minimal regions we can mine the correct net. We also briefly discuss on how transitions systems can be obtained by event logs","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129208767","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}
Guojian Cheng, Tianshi Liu, Kuisheng Wang, Jiaxin Han
Competitive learning can be defined as an adaptive process in which the neurons in an artificial neural network gradually become sensitive to different input categories which are sets of patterns in a specific domain of the input space. By using competitive learning, Kohonen's self-organizing maps (KSOM) can generate mappings from high-dimensional signal spaces to lower dimensional topological structures. The main features of KSOM are formation of topology preserving feature maps and approximation of input probability distribution. However, KSOM have some shortages, e.g., a fixed number of neural units and a fixed topology dimensionality which can result in problems if this dimensionality does not match the dimensionality of the feature manifold. Compared to KSOM, growing self-organizing neural networks (GSONN) can change their topological structures during learning. The topology formation of both GSONN and KSOM is driven by soft competitive learning. This paper first gives an introduction to KSOM and neural gas network. Then, we discuss some GSONN without fixed dimensionality such as growing neural gas and the author's model: twin growing neural gas and it's application for pattern classification. It is ended with some conclusions
{"title":"Soft Competitive Learning and Growing Self-Organizing Neural Networks for Pattern Classification","authors":"Guojian Cheng, Tianshi Liu, Kuisheng Wang, Jiaxin Han","doi":"10.1109/SYNASC.2006.68","DOIUrl":"https://doi.org/10.1109/SYNASC.2006.68","url":null,"abstract":"Competitive learning can be defined as an adaptive process in which the neurons in an artificial neural network gradually become sensitive to different input categories which are sets of patterns in a specific domain of the input space. By using competitive learning, Kohonen's self-organizing maps (KSOM) can generate mappings from high-dimensional signal spaces to lower dimensional topological structures. The main features of KSOM are formation of topology preserving feature maps and approximation of input probability distribution. However, KSOM have some shortages, e.g., a fixed number of neural units and a fixed topology dimensionality which can result in problems if this dimensionality does not match the dimensionality of the feature manifold. Compared to KSOM, growing self-organizing neural networks (GSONN) can change their topological structures during learning. The topology formation of both GSONN and KSOM is driven by soft competitive learning. This paper first gives an introduction to KSOM and neural gas network. Then, we discuss some GSONN without fixed dimensionality such as growing neural gas and the author's model: twin growing neural gas and it's application for pattern classification. It is ended with some conclusions","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125412961","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}
A set of algorithms designed for the analysis of phase-space perturbation propagation in dynamical systems is presented. The most important tasks are approximate volume estimation and determining the subset of the perturbation surface that is tangent to current lines. The algorithms are developed in a generic, dimension-independent fashion, but they can be partially evaluated with respect to the dimension of the space, yielding dimension specific, efficient algorithms. The MuPAD implementation of the algorithms and supporting libraries is also discussed
{"title":"A Two-Level Programming Approach to Volume Propagation in Higher-Dimensional Spaces","authors":"R. Zapotinschi, D. Peter","doi":"10.1109/SYNASC.2006.11","DOIUrl":"https://doi.org/10.1109/SYNASC.2006.11","url":null,"abstract":"A set of algorithms designed for the analysis of phase-space perturbation propagation in dynamical systems is presented. The most important tasks are approximate volume estimation and determining the subset of the perturbation surface that is tangent to current lines. The algorithms are developed in a generic, dimension-independent fashion, but they can be partially evaluated with respect to the dimension of the space, yielding dimension specific, efficient algorithms. The MuPAD implementation of the algorithms and supporting libraries is also discussed","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126349111","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}
In the last years, as object-oriented software systems became more and more complex, the need of performing automatically reverse engineering upon these systems has increased significantly. This applies also to enterprise applications, a novel category of software systems. As it is well known, one step toward a research infrastructure accelerating the progress of reverse engineering is the creation of an intermediate representation of software systems. This paper shows why existing intermediate representations of object-oriented software are not suitable for performing reverse engineering upon enterprise applications and proposes an intermediate representation (a model) for enterprise applications which facilitates the process of reverse engineering upon this type of applications. Based on an experimental study conducted on three enterprise applications, we prove the reliability of the introduced approach, discuss its benefits and touch the issues that need to be addressed in the future
{"title":"A Meta-Model for Enterprise Applications","authors":"C. Marinescu, I. Jurca","doi":"10.1109/SYNASC.2006.3","DOIUrl":"https://doi.org/10.1109/SYNASC.2006.3","url":null,"abstract":"In the last years, as object-oriented software systems became more and more complex, the need of performing automatically reverse engineering upon these systems has increased significantly. This applies also to enterprise applications, a novel category of software systems. As it is well known, one step toward a research infrastructure accelerating the progress of reverse engineering is the creation of an intermediate representation of software systems. This paper shows why existing intermediate representations of object-oriented software are not suitable for performing reverse engineering upon enterprise applications and proposes an intermediate representation (a model) for enterprise applications which facilitates the process of reverse engineering upon this type of applications. Based on an experimental study conducted on three enterprise applications, we prove the reliability of the introduced approach, discuss its benefits and touch the issues that need to be addressed in the future","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116567521","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}
In order to solve the algebraic equation, where the complex polynomial has only simple zeros, one can use the simultaneous inclusion methods. The quadratic convergence condition for the Durand-Kerner simultaneous inclusion method, using point estimation theory is w(0) < d(0)/(an+b), where n is the polynomial degree, d(0) the minimum distance between the initial iterations and w(0) is the absolute maximum of the Weierstrass factors. This paper determines the optimum quadratic convergence condition for a generalized Durand-Kerner type simultaneous inclusion method
{"title":"The Optimum Convergence Condition for the Durand-Kerner Type Simultaneous Inclusion Method","authors":"Octavian Cira, Cristian Cira","doi":"10.1109/SYNASC.2006.74","DOIUrl":"https://doi.org/10.1109/SYNASC.2006.74","url":null,"abstract":"In order to solve the algebraic equation, where the complex polynomial has only simple zeros, one can use the simultaneous inclusion methods. The quadratic convergence condition for the Durand-Kerner simultaneous inclusion method, using point estimation theory is w(0) < d(0)/(an+b), where n is the polynomial degree, d(0) the minimum distance between the initial iterations and w(0) is the absolute maximum of the Weierstrass factors. This paper determines the optimum quadratic convergence condition for a generalized Durand-Kerner type simultaneous inclusion method","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122431985","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}
Mining relational data often boils down to computing clusters, that is finding sub-communities of data elements forming cohesive sub-units, while being well separated from one another. The clusters themselves are sometimes terms "communities" and the way clusters relate to one another is often referred to as a "community structure". We study a modularity criterion MQ introduced by Mancoridis et al. in order to infer community structure on relational data. We prove a fundamental and useful property of the modularity measure MQ, showing that it can be approximated by a Gaussian distribution, making it a prevalent choice over less focused optimization criterion for graph clustering. This makes it possible to compare two different clusterings of a same graph as well as asserting the overall quality of a given clustering relying on the fact that MQ is Gaussian. Moreover, we introduce a generalization extending MQ to hierarchical clusterings of graphs which reduces to the original MQ when the hierarchy becomes flat
{"title":"A Quality Measure for Multi-Level Community Structure","authors":"M. Delest, J. Fedou, G. Melançon","doi":"10.1109/SYNASC.2006.9","DOIUrl":"https://doi.org/10.1109/SYNASC.2006.9","url":null,"abstract":"Mining relational data often boils down to computing clusters, that is finding sub-communities of data elements forming cohesive sub-units, while being well separated from one another. The clusters themselves are sometimes terms \"communities\" and the way clusters relate to one another is often referred to as a \"community structure\". We study a modularity criterion MQ introduced by Mancoridis et al. in order to infer community structure on relational data. We prove a fundamental and useful property of the modularity measure MQ, showing that it can be approximated by a Gaussian distribution, making it a prevalent choice over less focused optimization criterion for graph clustering. This makes it possible to compare two different clusterings of a same graph as well as asserting the overall quality of a given clustering relying on the fact that MQ is Gaussian. Moreover, we introduce a generalization extending MQ to hierarchical clusterings of graphs which reduces to the original MQ when the hierarchy becomes flat","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132749799","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}
A new learning technique based on cooperative coevolution is proposed for tackling classification problems. For each possible outcome of the classification task, a population of if-then rules, all having that certain class as the conclusion part, is evolved. Cooperation between rules appears in the evaluation stage, when complete sets of rules are formed with the purpose of measuring their classification accuracy on the training data. In the end of the evolution process, a complete set of rules is extracted by selecting a rule from each of the final populations. It is then applied to the test data. Some interesting results were obtained from experiments conducted on Fisher's iris benchmark problem
{"title":"Cooperative Evolution of Rules for Classification","authors":"C. Stoean, M. Preuss, D. Dumitrescu, R. Stoean","doi":"10.1109/SYNASC.2006.27","DOIUrl":"https://doi.org/10.1109/SYNASC.2006.27","url":null,"abstract":"A new learning technique based on cooperative coevolution is proposed for tackling classification problems. For each possible outcome of the classification task, a population of if-then rules, all having that certain class as the conclusion part, is evolved. Cooperation between rules appears in the evaluation stage, when complete sets of rules are formed with the purpose of measuring their classification accuracy on the training data. In the end of the evolution process, a complete set of rules is extracted by selecting a rule from each of the final populations. It is then applied to the test data. Some interesting results were obtained from experiments conducted on Fisher's iris benchmark problem","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133993947","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}
Web services are the technology of choice for Internet-based applications with loosely coupled clients and servers. The state is kept in an entity called resource. Web service resource framework is an open framework for modeling and accessing stateful resources using Web services. The paper approaches the main issues that should be taken into consideration during the design process of stateful Web services: using the factory design pattern for creating resources, registering resources in an index service, building a secure Web service, accessing resource properties through the methods exposed in interfaces, lifetime management and notifications
{"title":"The Design of Stateful Web Services Based on Web Service Resource Framework Implemented in Globus Toolkit 4","authors":"Maria Laura Sebu, H. Ciocarlie","doi":"10.1109/SYNASC.2006.73","DOIUrl":"https://doi.org/10.1109/SYNASC.2006.73","url":null,"abstract":"Web services are the technology of choice for Internet-based applications with loosely coupled clients and servers. The state is kept in an entity called resource. Web service resource framework is an open framework for modeling and accessing stateful resources using Web services. The paper approaches the main issues that should be taken into consideration during the design process of stateful Web services: using the factory design pattern for creating resources, registering resources in an index service, building a secure Web service, accessing resource properties through the methods exposed in interfaces, lifetime management and notifications","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"277 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116085386","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}
In this paper we revise some previously defined non-positional number encodings using multisets and their associated arithmetic operations, describing a general encoding/decoding algorithm that can map natural numbers to their multiset representations, and vice versa. We present the templates for the most compact encodings in base b for successor and predecessor operations
{"title":"Number Encodings and Arithmetics over Multisets","authors":"C. Bonchis, Cornel Izbasa, Gabriel Ciobanu","doi":"10.1109/SYNASC.2006.58","DOIUrl":"https://doi.org/10.1109/SYNASC.2006.58","url":null,"abstract":"In this paper we revise some previously defined non-positional number encodings using multisets and their associated arithmetic operations, describing a general encoding/decoding algorithm that can map natural numbers to their multiset representations, and vice versa. We present the templates for the most compact encodings in base b for successor and predecessor operations","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129568299","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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