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Wang D.-G.,Dalian University of Technology | Wang D.-G.,Informedia Electronic Co. | Song W.-Y.,Dongbei University of Finance and Economics | Shi P.,Victoria University of Melbourne | And 2 more authors.
Information Sciences | Year: 2013

In this paper, a dynamic fuzzy inference marginal linearization (DFIML) method is proposed for modeling nonlinear dynamic systems. This method can transfer a group of input-output data into a time-variant fuzzy system with variable coefficients. It is shown that solutions of time-variant fuzzy systems generalized by DFIML method are universal approximators to solutions of a class of non-autonomous systems. Also the analytical solutions of these time-variant fuzzy systems can be obtained. Finally, a simulation example is provided to illustrate the validity and potential of the developed techniques. © 2012 Elsevier Inc. All rights reserved.


Zou L.,Liaoning Normal University | Zou L.,Informedia Electronic Co. | Liu X.,Liaoning Normal University | Pei Z.,Xihua University | Huang D.,Dalian University of Technology
International Journal of Machine Learning and Cybernetics | Year: 2013

We construct a kind of linguistic truth-valued intuitionistic fuzzy lattice based on linguistic truth-valued lattice implication algebras to deal with linguistic truth values. We get some properties of implication operators on the set of ∨-irreducible elements. And furthermore the implication operators on the linguistic truth-valued intuitionistic fuzzy lattice are discussed. The proposed system can better express both comparable and incomparable information. Also it can deal with both positive and negative evidences which are represented by linguistic truth values at the same time during the information processing system. © 2012 Springer-Verlag.


Yin M.,Liaoning Normal University | Zou L.,Informedia Electronic Co. | Zou L.,Liaoning Normal University | Liu X.,Liaoning Normal University
ICIC Express Letters, Part B: Applications | Year: 2013

In classical logic the truth value of a proposition is true or false. Since there are many fuzzy concepts in the real world, the truth value of a fuzzy proposition is a real number in the interval [0, 1]. Is it a singleton true value for a given proposition? We will give a fuzzy proposition different truth values because of different people or different circumstances. A kind of qualitative fuzzy propositional logic system that can reflect the "elastic" of a fuzzy proposition is introduced. The truth value of fuzzy proposition is not singleton that depends on the context in the real world. Considering a fuzzy proposition one will choose the equivalence relation and get different class. Then based on an equivalence class, a qualitative fuzzy proposition set can be held. Then the resolution method of qualitative first-order logic is discussed. © 2013 ISSN 2185-2766.


Fang P.,Dalian University of Technology | Wang D.,Dalian University of Technology | Wang D.,Informedia Electronic Co. | Song W.,Dongbei University of Finance and Economics
ICIC Express Letters, Part B: Applications | Year: 2015

In this paper, a novel learning method is proposed for training the parameters of fuzzy wavelet neural network (FWNN). Extreme learning machine (ELM) is utilized to train the linear parameters of FWNN. And the gradient descent (GD) algorithm is used to update the nonlinear parameters. Some numerical examples show that the proposed learning algorithm can achieve high accuracy with fewer epochs. © 2015, ICIC Express Letters Office. All rights reserved.


Song W.,Dongbei University of Finance and Economics | Wang D.,Dalian University of Technology | Wang D.,Informedia Electronic Co. | Liu Y.,Dalian University of Technology
ICIC Express Letters, Part B: Applications | Year: 2014

In this paper, a fuzzy neural network (FNN) which utilizes Taylor expansion as the consequent of fuzzy rules is proposed for modeling and predicting nonlinear systems. Fuzzy c-means (FCM) method is used to determine the number of fuzzy rules. Extreme learning machine (ELM) is adopted to determine the parameters of fuzzy neural network. Further, a structure-learning algorithm is obtained to determine the number of fuzzy rules and the order of the Taylor expansion. Simulation results show that the proposed model can achieve good approximation capability for some nonlinear systems with simpler structure. © 2014 ICIC International.

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