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ABSTRACT: While fuzzy systems can advantageously be used in system modeling and control, their use in time-critical applications is limited because of complexity problems, especially in cases when not only low, but also flexibly changeable complexity is needed. Previously, a method has been proposed to use fuzzy and other soft-computational tools in the frame of modular anytime architectures; however, the applicability needs the a priori knowledge of the temporarily available time and resources. This paper proposes a new transformation method, which makes possible the iterative-type evaluation of product-sum-gravity-singleton (PSGS) fuzzy systems, with a really flexibly changeable complexity, and with an easily estimable error at any step of the evaluation. Moreover, the transformation also ensures the fastest possible decrease of the error.
IEEE Transactions on Instrumentation and Measurement 03/2005; · 1.21 Impact Factor
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ABSTRACT: With the help of the SVD-based (singular value decomposition) complexity reduction method, not only the redundancy of fuzzy rule-bases are eliminated, but also further, nonexact reduction are made, considering the allowable error. Namely, in case of higher allowable error, the result is a less complex fuzzy inference system, with a smaller rule-base. This property of the SVD-based reduction method makes possible the usage of fuzzy systems in time-critical applications and makes possible the combining of fuzzy systems with anytime techniques to cope with the changing circumstances during the operation of the system. However, while the SVD-based reduction can be applied to PSGS fuzzy systems, in case of rule-bases, constructed from expert knowledge, the input fuzzy sets are not always in Ruspini-partition. This paper extends the SVD-based reduction to "near PSGS" fuzzy systems, where the input fuzzy sets are not in Ruspini-partition.
Intelligent Signal Processing, 2003 IEEE International Symposium on; 10/2003
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ABSTRACT: With the help of the singular value decomposition (SVD) based
complexity reduction method, not only can the redundancy of fuzzy
rule-bases be eliminated, but further reduction can also be made,
considering the allowable error. Namely, in the case of higher allowable
error, the result may be a less complex fuzzy inference system, with a
smaller rule-base. This property of the SVD-based reduction method makes
possible the usage of fuzzy systems, even in cases when the available
time and resources are limited. The original SVD-based reduction method
was proposed for rule-bases with linear antecedent fuzzy sets. This
limitation remained valid in the later extensions, as well. The purpose
of this paper is to give a formal mathematical proof for the original
formulas with nonlinear antecedent fuzzy sets and thus to end this
limitation
IEEE Transactions on Instrumentation and Measurement 05/2002; · 1.21 Impact Factor
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ABSTRACT: While fuzzy systems can advantageously be used in system modeling and control, their use in time-critical applications is limited because of complexity problems, especially in cases, when not only low, but also flexibly changeable complexity is needed. Previously a method has been proposed to use fuzzy and other soft-computational tools in the frame of modular anytime architectures; however, the applicability needs the a priori knowledge of the temporarily available time and resources. This paper proposes a new transformation method, which makes possible the iterative-type evaluation of PSGS fuzzy systems, with a really flexibly changeable complexity, and with an easily estimable error at any step of the evaluation. Moreover, the transformation also ensures the fastest possible decrease of the error.
Instrumentation and Measurement Technology Conference, 2002. IMTC/2002. Proceedings of the 19th IEEE; 02/2002
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ABSTRACT: Model based techniques play very important role in solving engineering problems. Recently, for representing nonlinear systems fuzzy models became very popular for evaluating measurement data and controller design, and the inverse models are of considerable interest. In this paper the extension of the observer based technique to perform fuzzy model inversion is presented. The inversion can be extended towards anytime modes of operation providing short response time and flexibility during temporal loss of computational power and/or time.
Fuzzy Systems, 2001. The 10th IEEE International Conference on; 01/2002
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ABSTRACT: In modern measurement, control, monitoring and fault diagnosis systems, there is an increasing need for the use of non-classical computing methods. On the other hand, in these systems the available time and resources are usually limited, so methods with lower computational complexity are needed. Thus, the need arises to have formal methods for the complexity reduction of different soft-computing techniques. This paper discusses a possible method for the non-exact reduction of generalized type neuro-fuzzy systems, and gives the necessary error-bounds of the reduction.
Fuzzy Systems, 2001. The 10th IEEE International Conference on; 01/2002
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ABSTRACT: The main advantage of neural networks (NNs) is that they are able
to solve complicated problems, even if the exact mathematical model is
not known. However, there is no universal method for the approximation
of the proper size of the neural networks which usually results in the
overestimation of the needed size. Therefore, the need arises to have
formal methods for the complexity reduction of neural networks. Singular
Value Decomposition (SVD) based complexity reduction was first proposed
for various fuzzy inference systems. Recently, the method has been
extended to generalized neural network, which made possible the use of
neural networks in time-critical systems. Beyond the elimination of
redundancy, the SVD-based reduction can be used to achieve further
reduction, if a certain amount of error can be tolerated. This paper
gives an error-bound for this further complexity reduction of
generalized type hybrid neural networks with non-singleton consequents
Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE; 02/2001
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ABSTRACT: In modern measurement and control systems, the available time and
resources are often not only limited, but could change during the
operation of the system. In these cases, the so called anytime
algorithms could be used advantageously. While different soft computing
methods are in widespread use in system modeling, their usability in
these cases are limited, because the lack of a universal method for the
determination of the needed complexity often results in huge and
redundant neural networks/fuzzy rule-bases. This paper proposes a
possible way to carry out anytime information processing in fuzzy
systems or neural networks, with the help of the SVD-based complexity
reduction algorithm
Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE; 02/2001
