An orthogonal least-squares method for recurrent fuzzy-neural modeling

Mastorocostas, Paris A.; Theocharis, John B.

doi:10.1016/s0165-0114(02)00523-7

Cited by 17 publications

(18 citation statements)

References 26 publications

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“…In this perspective, the fuzzy inference system employed to perform system identification is the DN-FNN [15]. The classic Takagi-Sugeno-Kang model (TSK) consists of a set of linguistic IF-THEN rules with polynomial consequent parts [16]:…”

Section: The Dn-fnn Modelmentioning

confidence: 99%

“…The DN is characterized by the orders of the infinite impulse response (IIR) synapse, O u and i; thus the formalism DN(O u , i) fully determines a dynamic neuron. As mentioned in [15], the DN-FNN is a generalized TSK dynamic model with a Locally-Recurrent-GloballyFeedforward structure [18]. The rules are not linked with each other in time; either through external or internal feedbacks; they are connected merely via the defuzzification part.…”

Section: The Dn-fnn Modelmentioning

confidence: 99%

“…The D-OLS is fully described in [15]. In brief, the method aims at developing DN-FNN models by transforming them to autoregressive models.…”

Section: The D-ols Algorithmmentioning

confidence: 99%

“…The filter is implemented by the dynamic neuron-based fuzzy neural network (DN-FNN), which has been proposed in [15] as an efficient identification tool.…”

Section: Introductionmentioning

confidence: 99%

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A generalized Takagi–Sugeno–Kang recurrent fuzzy-neural filter for adaptive noise cancelation

Mastorocostas

Varsamis

Hilas

et al. 2007

Neural Comput Applic

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This paper presents a recurrent fuzzy-neural filter for adaptive noise cancelation. The cancelation task is transformed to a system-identification problem, which is tackled by use of the dynamic neuron-based fuzzy neural network (DN-FNN). The fuzzy model is based on Takagi-Sugeno-Kang fuzzy rules, whose consequent parts consist of linear combinations of dynamic neurons. The orthogonal least squares method is employed to select the number of rules, along with the number and kind of dynamic neurons that participate in each rule. Extensive simulation results are given and performance comparison with a series of other dynamic fuzzy and neural models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.

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Section: The Dn-fnn Modelmentioning

confidence: 99%

Section: The Dn-fnn Modelmentioning

confidence: 99%

“…The D-OLS is fully described in [15]. In brief, the method aims at developing DN-FNN models by transforming them to autoregressive models.…”

Section: The D-ols Algorithmmentioning

confidence: 99%

“…The filter is implemented by the dynamic neuron-based fuzzy neural network (DN-FNN), which has been proposed in [15] as an efficient identification tool.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A generalized Takagi–Sugeno–Kang recurrent fuzzy-neural filter for adaptive noise cancelation

Mastorocostas

Varsamis

Hilas

et al. 2007

Neural Comput Applic

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show abstract

“…(1) Orthogonality is also an important tool in traditional fuzzy systems. Orthogonal transformation method, orthogonal rule, and orthogonal approximation concept are frequently applied to fuzzy rule-based models [26][27][28], fuzzy neural networks [29,30], and fuzzy control [31,32]. Complex fuzzy sets as an extension of fuzzy sets have been studied.…”

Section: Introductionmentioning

confidence: 99%

The Orthogonality between Complex Fuzzy Sets and Its Application to Signal Detection

Dai

2017

Symmetry

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Abstract:A complex fuzzy set is a set whose membership values are vectors in the unit circle in the complex plane. This paper establishes the orthogonality relation of complex fuzzy sets. Two complex fuzzy sets are said to be orthogonal if their membership vectors are perpendicular. We present the basic properties of orthogonality of complex fuzzy sets and various results on orthogonality with respect to complex fuzzy complement, complex fuzzy union, complex fuzzy intersection, and complex fuzzy inference methods. Finally, an example application of signal detection demonstrates the utility of the orthogonality of complex fuzzy sets.

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Fuzzy Modeling Technique with PSO Algorithm for Short-Term Load Forecasting

Sun

2006

Fuzzy Systems and Knowledge Discovery

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An orthogonal least-squares method for recurrent fuzzy-neural modeling

Cited by 17 publications

References 26 publications

A generalized Takagi–Sugeno–Kang recurrent fuzzy-neural filter for adaptive noise cancelation

A generalized Takagi–Sugeno–Kang recurrent fuzzy-neural filter for adaptive noise cancelation

The Orthogonality between Complex Fuzzy Sets and Its Application to Signal Detection

Fuzzy Modeling Technique with PSO Algorithm for Short-Term Load Forecasting

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