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

Bodyanskiy, Yevgeniy; Pliss, Iryna; Vynokurova, Olena

doi:10.2478/itms-2013-0007

Cited by 11 publications

(10 citation statements)

References 11 publications

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“…In addition to Gaussians (1), other nuclear functions can be used, for example, B-splines that meet the condition of a single breakdown, paired wavelets, flexible activation-membership functions [18,19], etc.…”

Section: Formalization Of the Task Of Training Artificial Neural Networkmentioning

confidence: 99%

Development of an algorithm to train artificial neural networks for intelligent decision support systems

Sova

Turinskyi

Shyshatskyi

et al. 2020

EEJET

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The algorithm to train artificial neural networks for intelligent decision support systems has been constructed. A distinctive feature of the proposed algorithm is that it conducts training not only for synaptic weights of an artificial neural network, but also for the type and parameters of membership function. In case of inability to ensure the assigned quality of functioning of artificial neural networks due to training of parameters of artificial neural network, the architecture of artificial neural networks is trained. The choice of the architecture, type and parameters of membership function occurs taking into consideration the computation resources of the facility and taking into consideration the type and the amount of information entering the input of an artificial neural network. In addition, when using the proposed algorithm, there is no accumulation of an error of artificial neural networks training as a result of processing the information entering the input of artificial neural networks.Development of the proposed algorithm was predetermined by the need to train artificial neural networks for intelligent decision support systems in order to process more information given the unambiguity of decisions being made. The research results revealed that the specified training algorithm provides on average 16–23 % higher the efficiency of training artificial neural networks training that is on average by 16–23 % higher and does not accumulate errors in the course of training. The specified algorithm will make it possible to conduct training of artificial neural networks; to determine effective measures to enhance the efficiency of functioning of artificial neural networks. The developed algorithm will also enable the improvement of the efficiency of functioning of artificial neural networks due to training the parameters and the architecture of artificial neural networks. The proposed algorithm reduces the use of computational resources of decision support systems. The application of the developed algorithm makes it possible to work out the measures aimed at improving the effectiveness of training artificial neural networks and to increase the efficiency of information processing

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Section: Formalization Of the Task Of Training Artificial Neural Networkmentioning

confidence: 99%

Development of an algorithm to train artificial neural networks for intelligent decision support systems

Sova

Turinskyi

Shyshatskyi

et al. 2020

EEJET

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show abstract

“…The work [18] proposed an original online evolving fuzzy clustering method (EFCM) based on a probabilistic approach to solve a problem. The main parameter that ultimately determines the final result is the radius of the formed clusters, selected from empirical considerations and ultimately determines the number of possible classes.…”

Section: Initialization By Examples When the Values Of Randomly Selected Examples From The Training Sample Are Given As Initial Values;mentioning

confidence: 99%

“…then the centroid is corrected according to the WTA (winner-takes-all) Kohonen self-learning rule [18]:…”

Section: Initialization By Examples When the Values Of Randomly Selected Examples From The Training Sample Are Given As Initial Values;mentioning

confidence: 99%

Development of a methodology for training artificial neural networks for intelligent decision support systems

Sova

Shyshatskyi

Zhuravskyi

et al. 2020

EEJET

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“…A integração entre Sistemas Fuzzy e Redes Neurais tem sido amplamente explorada para resolução de diversos problemas, principalmente, no processamento de dados estocásticos e nãolineares [1]. Esta integração tem o objetivo de empregar o tratamento de incerteza e a interpretabilidade dos Sistemas Fuzzy, aprimorando sua habilidade de aprendizado através das características presentes nas Redes Neurais [2].…”

Section: Introductionunclassified

Uma Nova Abordagem para Construção de Regras em Redes Neo-Fuzzy-Neuron Utilizando Programação Genética Multi-Gene

Brás¹,

Silva²

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Resumo-Este artigo propõe a utilização de Programação Genética Multi-Gene (PG-MG) para criação de regras em Redes Neo-Fuzzy-Neuron (NFN). NFNé uma rede Neuro-Fuzzy composta por n estruturas de Takagi-Sugeno de ordem zero, onde né o número de variáveis de entrada. A saída individual de cada uma das n estruturasé dada por um conjunto de m regras, em que cada regraé representada por uma função de pertinência Gaussiana. Desta forma, a construção da base de regras na NFN consiste em criar e ajustar as funções de pertinência associadas a cada variável de entrada. A abordagem proposta utiliza a Programação Genética Multi-Gene (PG-MG) para gerar as regras (conjuntos fuzzy do antecedente), na qual cada indivíduo da PG-MG representa um conjunto de regras que compõe o modelo. Por fim, os pesos (parâmetros do consequente) são ajustados por um método do Gradiente Descendente. A abordagem propostaé avaliada em problemas de previsão e os resultados obtidos sugerem que a abordagem propostaé competitiva quando comparada com modelos alternativos do estado da arte.

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

Cited by 11 publications

References 11 publications

Development of an algorithm to train artificial neural networks for intelligent decision support systems

Development of an algorithm to train artificial neural networks for intelligent decision support systems

Development of a methodology for training artificial neural networks for intelligent decision support systems

Uma Nova Abordagem para Construção de Regras em Redes Neo-Fuzzy-Neuron Utilizando Programação Genética Multi-Gene

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