A neo-fuzzy-neuron with real time training applied to flux observer for an induction motor

Landim, Regis Pinheiro; Rodrigues, Beatriz Araujo; Silva, S.R.; Caminhas, Walmir M.

doi:10.1109/sbrn.1998.730996

Cited by 16 publications

(3 citation statements)

References 8 publications

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“…PROPOSED NETWORK As a "building block"-stack of the system under consideration, we propose to use the generalized neo-fuzzyneuron (GNFN) [29], that is a generalization of the neofuzzy neuron (NFN) [16][17][18] for the multidimensional case. In Fig.…”

Section: Generalized Neo-fuzzy-neuron As Stack Ofmentioning

confidence: 99%

Deep Evolving Stacking Convex Cascade Neo-Fuzzy Network and Its Rapid Learning

Setlak¹,

Bodvanskiy²,

Pliss³

et al. 2018

Annals of Computer Science and Information Systems

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A deep evolving stacking convex neo-fuzzy network is proposed. It is a feedforward cascade hybrid system, the layers-stacks of which are formed by generalized neo-fuzzy neurons that implement Wang-Mendel fuzzy reasoning. The optimal in the sense of speed algorithms are proposed for its learning. Due to independent layer adjustment, parallelization of calculations in non-linear synapses and optimization of learning processes, the proposed network has high speed that allows to process information in online mode.

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Section: Generalized Neo-fuzzy-neuron As Stack Ofmentioning

confidence: 99%

Deep Evolving Stacking Convex Cascade Neo-Fuzzy Network and Its Rapid Learning

Setlak¹,

Bodvanskiy²,

Pliss³

et al. 2018

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…In the same time the hybrid systems of computational intelligence with many inputs and many outputs, which are constructed based on neo-fuzzy neurons, have an abundant number of membership functions. It is possible significantly to reduce the number of these functions, using the so-called generalized neo-fuzzy neuron (GNFN) [18]. GNFN is the extension of NFN for a multidimension case and contains less number of membership functions.…”

Section: Introductionmentioning

confidence: 99%

The Autoencoder Based on Generalized Neo-Fuzzy Neuron and its Fast Learning for Deep Neural Networks

Bodyanskiy

Peleshko

Rashkevych

et al. 2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

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In this paper the autoencoder based on the generalized neo-fuzzy neurons is proposed. Also its fast learning algorithm based on quadratic criterion was proposed. Such system can be used as part of deep learning systems. The proposed autoencoder is characterized by high learning speed and less number of tuned parameters in comparison with wellknown autoencoders of "bottle neck" type. The efficiency of proposed approach has been justified based on different benchmarks and real data sets.

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“…For example, the extended NFN was proposed in [2], and in [5], [6] the same authors proposed the usage of wavelets instead of triangular membership functions, that led to the emergence of a wavelet neuron. Since NFN is a system with one output, which limits its capabilities, in [7]- [9] a generalized NFN was proposed to the case of multiple outputs, which allowed restoring of nonlinear mappings .…”

Section: Introductionmentioning

confidence: 99%

2D-Neo-Fuzzy Neuron and Its Adaptive Learning

Bodyanskiy

Vynokurova

Volkova

et al. 2018

ITMS

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In the paper 2D-neo-fuzzy neuron is presented. It is a generalization of the traditional NFN for data in matrix form. 2D-NFN is based on the matrix adaptive bilinear model with an additional fuzzification layer. It reduces the number of adjustable synaptic weights in comparison with traditional systems. For its learning, optimized adaptive procedures with filtering and tracking properties are proposed. 2D-NFN can be effectively used for image processing, data reduction, and restoration of non-stationary signals, presented as 2D-sequences.

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A neo-fuzzy-neuron with real time training applied to flux observer for an induction motor

Cited by 16 publications

References 8 publications

Deep Evolving Stacking Convex Cascade Neo-Fuzzy Network and Its Rapid Learning

Deep Evolving Stacking Convex Cascade Neo-Fuzzy Network and Its Rapid Learning

The Autoencoder Based on Generalized Neo-Fuzzy Neuron and its Fast Learning for Deep Neural Networks

2D-Neo-Fuzzy Neuron and Its Adaptive Learning

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