Soft Computing Based Signal Prediction, Restoration, and Filtering

Uchino, Eiji; Yamakawa, Takeshi

doi:10.1007/978-1-4615-6191-0_14

Cited by 41 publications

(26 citation statements)

References 9 publications

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“…1) was proposed in [7], [8], [9]. As it can be seen, the architecture of neo-fuzzy neuron is quite close to the conventional n -inputs artificial neuron; however, instead of usual synaptic weights ji w it contains the so-called nonlinear synapses , 1, 2, ,…”

Section: Neo-fuzzy Neuron and Its Architecturementioning

confidence: 99%

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Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties

Bodyanskiy

Pliss

Vynokurova

2013

Information Technology and Management Science

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-In the paper, a new flexible modification of neofuzzy neuron, neuro-fuzzy network based on these neurons and adaptive learning algorithms for the tuning of their all parameters are proposed. The algorithms are of interest because they ensure the on-line tuning of not only the synaptic weights and membership function parameters but also forms of these functions that provide improving approximation properties and allow avoiding the occurrence of "gaps" in the space of inputs.

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Section: Neo-fuzzy Neuron and Its Architecturementioning

confidence: 99%

“…The authors of neo-fuzzy neuron note [9] that among its most important advantages there are the rate of learning, high approximation properties, computational simplicity, the possibility of finding the global minimum of the learning criterion in real time.…”

Section: Neo-fuzzy Neuron and Its Architecturementioning

confidence: 99%

Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties

Bodyanskiy

Pliss

Vynokurova

2013

Information Technology and Management Science

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“…To overpass some of the outlined above problems, a neuro-fuzzy system also known as a -neo-fuzzy neuron‖ (NFN) was taken into consideration and explored in [11][12][13]. Fig.1 gives a demonstration of the neo-fuzzy neuron's organization.…”

Section: Introductionmentioning

confidence: 99%

“…This framework generally brings into action a presentation   NFN's inventors [11][12][13] brought into requisition common triangular frameworks as membership functions that meet the requirements of the unity partition.…”

Section: Introductionmentioning

confidence: 99%

A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition

Hu¹,

Bodyanskiy²,

Kulishova³

et al. 2017

IJISA

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Abstract-An article introduces a modified architecture of the neo-fuzzy neuron, also known as a "multidimensional extended neo-fuzzy neuron" (MENFN), for the face recognition problems. This architecture is marked by enhanced approximating capabilities. A characteristic property of the MENFN is also its computational plainness in comparison with neuro-fuzzy systems and neural networks. These qualities of the proposed system make it effectual for solving the image recognition problems. An introduced MENFN's adaptive learning algorithm allows solving classification problems in a real-time fashion.

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“…The NFN authors [8][9][10] used traditional triangular constructions as membership functions that meet the unity partition conditions …”

Section: The Neo-fuzzy Neuron Architecturementioning

confidence: 99%

An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm

Bodyanskiy¹,

Tyshchenko²,

Kopaliani³

2015

IJISA

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-A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties and solves prediction, filtering and smoothing tasks of non-stationary "noisy" stochastic and chaotic signals. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.

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Soft Computing Based Signal Prediction, Restoration, and Filtering

Cited by 41 publications

References 9 publications

Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties

Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties

A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition

An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm

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