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检索条件"任意字段=Proceedings of the 9th International Conference on Predictive Models in Software Engineering"

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Identification and control of dynamical systems based on cause-effect fuzzy models

Identification and control of dynamical systems based on cau...

引用

conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： G. Vachkov T. Fukuda Department of Micro System Engineering University of Nagoya Nagoya Japan Center for Cooperative Research in Advanced Science and Technology University of Nagoya Nagoya Japan

An incremental type of dynamical system model, based on cause-effect relationships is proposed. It is a fuzzy-like model where the relationships between the change-of-past-inputs and change-of-the-output is represented by a fuzzy membership function. the shape of the membership function directly affects the type of the modeled dynamics. three identification schemes using the least mean squares algorithm and its modifications are discussed and analyzed as follows: direct identification, reduced size indirect identification, and the newly proposed soft-guided identification. the indirect identification is achieved by tuning a simple one-dimensional Takagi-Sugeno model that represents in an indirect way the cause-effect relations in the dynamical system. the soft guided identification uses a human specified reference model and makes a kind of compromise solution between the pure data fitting and pure model fitting.

关键词： Control system synthesis Fuzzy control Fuzzy systems Fuzzy sets predictive models Sampling methods Electronic mail Systems engineering and theory Shape Least mean square algorithms

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Fuzzy regression models to represent electricity market data in deregulated power industry

Fuzzy regression models to represent electricity market data...

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conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： T. Niimura M. Dhaliwal K. Ozawa Department of Electrical and Computer Engineering University of British Columbia Vancouver B.C. Canada Faculty of Economics Hosei University Tokyo Japan

the authors present a flexible model that represents the relation of electricity price and demand in an electrical power market. Power market data are first analyzed by regression analysis. the price data show an upward trend as the demand volume increases. We have divided the regression model into two regions: low demand and high demand. Two curves are smoothly connected by a TSK-fuzzy model, noting the fact that the "low" demand and "high" demand regions are not distinct but overlapping. the fuzzy model is further extended to encompass the data region indicating the degree of possibility. California Power Exchange data are analyzed as an example.

关键词： Electricity supply industry Electricity supply industry deregulation Power industry Regression analysis Power markets Data analysis Economic forecasting predictive models Polynomials Supply and demand

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A formal approach to the assessment and improvement of terminological models used in information systems engineering 9

A formal approach to the assessment and improvement of termi...

引用

proceedings of the 8th European software engineering conference held jointly with 9th ACM SIGSOFT international symposium on Foundations of software engineering

作者： Peter Wendorff ASSET GmbH Am Flasdieck 5 46147 Oberhausen Germany

ISBN: (纸本)9781581133905

In the design and implementation of any information system identifiers are used to designate concepts. Typical examples are names of classes, variables, modules, database fields, etc. A terminological model is a set of identifiers together with a set of abstractions and a set of links between identifiers and abstractions. Naturally, terminological models embody important knowledge of a system, and therefore they play an important role during the development of information systems. In this paper we propose a metamodel for terminological models that is based on category theory as conceptual and notational framework.

关键词： identifier concept abstraction naming terminology denotation

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Support vector recurrent neurofuzzy networks in modeling nonlinear systems with correlated noise

Support vector recurrent neurofuzzy networks in modeling non...

引用

conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： W.C. Chan C.W. Chan K.C. Cheung C.J. Harris Department of Mechanical Engineering University of Hong Kong Hong Kong China Department of Electronics and Computing University of Southampton UK

Good generalization results are obtained from neurofuzzy networks if its structure is suitably chosen. To select the structure of neurofuzzy networks, the authors proposed a construction algorithm that is derived from the support vector regression. However, the modeling errors are assumed to be uncorrelated. In this paper, systems with correlated modeling errors are considered. the correlated noise is modeled separately by a recurrent network. the overall network is referred to as the support vector recurrent neurofuzzy networks. the prediction error method is used to train the networks, where the derivatives are computed by a sensitivity model. the performance of proposed networks is illustrated by an example involving a nonlinear dynamic system corrupted by correlated noise.

关键词： Intelligent networks Nonlinear systems Computer networks Neural networks Training data Cost function White noise Stochastic systems Mechanical engineering predictive models

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Model-based multiobjective fuzzy control using a new multiobjective dynamic programming approach

Model-based multiobjective fuzzy control using a new multiob...

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conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Dong-Oh Kang Zeungnam Bien Department of Electrical Engineering KAIST Daejeon South Korea

ISBN: (纸本)0780370783

the authors propose a model-based multiobjective fuzzy control method which is optimized online via a novel multiobjective dynamic programming. the new multiobjective dynamic programming is guaranteed to derive a Pareto optimal solution. To estimate the effect of each candidate for control input in the dynamic programming procedure, we use state-value predictors of multiple objectives based on the plant model. Temporal difference learning and supervised learning are used for update of the predictors and the plant model. As the learning proceeds, the proposed method derives the compromised solution among multiple objectives. To show the effectiveness of the proposed method, some simulation results are given.

关键词： Fuzzy control Dynamic programming Automatic control predictive models Control systems Fuzzy sets Pareto optimization Optimization methods Linear programming State estimation

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A compensatory wavelet neuron model

A compensatory wavelet neuron model

引用

conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： M. Sinha M.M. Gupta P.N. Nikiforuk College of Engineering University of Saskatchewan Saskatoon CANADA

the paper proposes a compensatory wavelet neural model, which is based on a wavelet activation function. the basis function comprises both summation and multiplicative functions. It is shown by M. Sinha et al. that, for a spectrum of functional mapping and classification problems, the compensatory neuron based neural network model performs better than the ordinary neuron based neural network, in terms of both the accuracy of prediction and the computational time involved. On the other hand, the wavelet neuron is obtained by modifying an ordinary neuron with non-orthogonal wavelet bases (T. Yamakawa et al., 1994). the performances of different neuron based neural networks are also analyzed.

关键词： Neurons Neural networks Convergence Feedforward neural networks Nonlinear equations Intelligent systems Laboratories Educational institutions predictive models Accuracy

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the hybrid multi-layer inference architecture and algorithm of FPNN based on FNN and PNN

The hybrid multi-layer inference architecture and algorithm ...

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conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Byoung-Jun Park Sung-Kwun Oh W. Pedrycz School of Electrical and Electronic Engineering Wonkwang University Iksan Gyeongbuk South Korea School of Electrical and Electronic Engineering Wonkwang University Iksan Chon-Buk South Korea Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

the study is concerned with an approach to the design of a new category of fuzzy neural networks. the proposed Fuzzy Polynomial Neural Networks (FPNN) with hybrid multi-layer inference architecture is based on fuzzy neural networks (FNN) and polynomial neural networks (PNN) for model identification of complex and nonlinear systems. the one and the other are considered as premise and consequence part of FPNN respectively. therefore, the proposed FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN and PNN. We introduce two kinds of FPNN architectures, namely the basic and modified architectures depending on the connection points (nodes) of the layer of FNN. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process and to get output performance with superb predictive ability. the availability and feasibility of the FPNN is discussed and illustrated with the aid of two representative numerical examples. the results show that the proposed FPNN can produce a model with higher accuracy and predictive ability than any other method presented previously.

关键词： Inference algorithms Fuzzy neural networks Polynomials Neural networks Multi-layer neural network Fuzzy systems Nonlinear systems Availability predictive models Accuracy

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Creating metabolic and regulatory network models using fuzzy cognitive maps

Creating metabolic and regulatory network models using fuzzy...

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conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： J.A. Dickerson Z. Cox E.S. Wurtele A.W. Fulmer Electrical Engineering Department Iowa State University Ames IA USA Botany Department Iowa State University Ames IA USA Corporate Research Procter and Gamble Company Cincinnati OH USA

this paper describes a model of metabolic networks that uses fuzzy cognitive maps. Nodes of the map represent specific biochemicals such as proteins, RNA, and small molecules, or stimuli, such as light, heat, or nutrients. Edges of the map capture regulatory and metabolic relationships found in biological systems. these relationships are established by a domain expert, the biological literature, and extracted from RNA microarray data. this work is part of the development of a software tool, FCModeler, which models and visualizes metabolic networks. A model of the metabolism of the plant hormone gibberellin in Arabidopsis is used to show the capabilities of the fuzzy model.

关键词： Fuzzy cognitive maps Biochemistry Biological system modeling RNA Gene expression Power system modeling Proteins Differential equations Petri nets Chemical processes

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Stability of slowly varying Takagi-Sugeno fuzzy systems

Stability of slowly varying Takagi-Sugeno fuzzy systems

引用

conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： R. Pytelkova P. Husek Department of Contrl Engineering Faculty of Electrical Engineering áCzech Technical University of Prague Praque Czech Republic

Presents a method for analyzing the stability of slowly varying Takagi-Sugeno fuzzy systems with linear submodels in the consequents of rules. this method can be used for both continuous-time and discrete-time systems and is based on transformation of the problem of stability of Takagi-Sugeno fuzzy systems to the problem of stability of polynomials with coefficients polynomically depending on weights of rules. It is supposed that the plant is described by the Takagi-Sugeno fuzzy system with linear state-space or input-output submodels in the consequents of rules and the controller by the Takagi-Sugeno fuzzy system with linear state feedback submodels or dynamic output feedback controllers in the consequents of rules. the problem of the stability analysis of such systems can be transformed to the problem of the stability analysis of polynomials with polynomic structure of its coefficients. this problem can be solved by the modified Jury (for discrete-time systems) or by the modified Routh or Hurwitz criterion (for continuous-time systems).

关键词： Takagi-Sugeno model Fuzzy systems Stability analysis Fuzzy control Polynomials predictive models Linear feedback control systems Control systems Nonlinear dynamical systems Fuzzy logic

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Adaptive neuro-fuzzy control of systems with time delay

Adaptive neuro-fuzzy control of systems with time delay

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conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： H.F. Ho Y.K. Wong A.B. Rad Department of Electrical Engineering Hong Kong Polytechnic University Hong Kong China

the authors present an adaptive fuzzy logic controller, which learns about the dynamic of the system under control from an online neural network (NN) identification algorithm. the identification is based on the estimation of parameters of a First-Order-Plus-Dead-Time (FOPDT) model. the outputs of the NN are three parameters: gain, apparent time delay and the dominant time constant. By combining this algorithm with a fuzzy logic controller with rotating rule-table, an adaptive controller is obtained which, with very little a priori knowledge, can compensate systems with time delay. the simplicity and feasibility of the scheme for time delay control provides a new approach for a variety of control applications. Simulation results are included to demonstrate the adaptive property of the proposed scheme.

关键词： Programmable control Adaptive control Control systems Delay effects Neural networks Fuzzy logic Parameter estimation Transfer functions Delay estimation predictive models

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