Back-propagation algorithm which varies the number of hidden units

Hirose, Yoshio; Yamashita, Koichi; Hijiya, Shimpei

doi:10.1016/0893-6080(91)90032-z

Cited by 435 publications

(132 citation statements)

References 1 publication

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“…For example, a neuron can be automatically created or removed from the hidden layer according to some conditions. For example, the threshold value can be compared to the training error rate [3,20], or a set of rules can be created based on the error values [6]. The result of the comparison or the satisfaction of the rules can be used to adjust the number of neurons.…”

Section: Qmentioning

confidence: 99%

Binary classification using ensemble neural networks and interval neutrosophic sets

Kraipeerapun

Fung

2009

Neurocomputing

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This paper presents an ensemble neural network and interval neutrosophic sets approach to the problem of binary classification. A bagging technique is applied to an ensemble of pairs of neural networks created to predict degree of truth membership, indeterminacy membership, and false membership values in the interval neutrosophic sets. In our approach, the error and vagueness are quantified in the classification process as well. A number of aggregation techniques are proposed in this paper. We applied our techniques to the classical benchmark problems including ionosphere, pimaIndians diabetes, and liver-disorders from the UCI machine learning repository. Our approaches improve the classification performance as compared to the existing techniques which applied only to the truth membership values. Furthermore, the proposed ensemble techniques also provide better results than those obtained from only a single pair of neural networks.

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Section: Qmentioning

confidence: 99%

Binary classification using ensemble neural networks and interval neutrosophic sets

Kraipeerapun

Fung

2009

Neurocomputing

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“…11, 16,17 Com base nos limites estabelecidos pela Portaria nº 309 da ANP, 2 foi testada a aplicação de redes neurais artificiais do tipo perceptron de múltiplas camadas, para a classificação de gasolinas em A, C e C adulterada e, ainda com base nas respostas da rede, estabelecer as características das gasolinas comercializadas na região de Londrina.…”

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Aplicação de redes neurais artificiais na identificação de gasolinas adulteradas comercializadas na região de Londrina - Paraná

Borsato¹,

Moreira²,

Nobrega³

et al. 2009

Quím. Nova

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“…Back-propagation (BP) [5] is the most popular ANN training algorithm. It is a gradient descent method that aims at minimizing the total root mean squared error (RMSE) between actual and desired outputs of a network by employing gradient information.…”

Section: Introductionmentioning

confidence: 99%

Evolving Resilient Back-Propagation Algorithm for Energy Efficiency Problem

Yang

Gao

2016

MATEC Web Conf.

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Abstract. Energy efficiency is one of our most economical sources of new energy. When it comes to efficient building design, the computation of the heating load (HL) and cooling load (CL) is required to determine the specifications of the heating and cooling equipment. The objective of this paper is to model heating load and cooling load buildings using neural networks in order to predict HL load and CL load. Rprop with genetic algorithm was proposed to increase the global convergence capability of Rprop by modifying a corresponding weight. Comparison results show that Rprop with GA can successfully improve the global convergence capability of Rprop and achieve lower MSE than other perceptron training algorithms, such as Back-Propagation or original Rprop. In addition, the trained network has better generalization ability and stabilization performance.

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Back-propagation algorithm which varies the number of hidden units

Cited by 435 publications

References 1 publication

Binary classification using ensemble neural networks and interval neutrosophic sets

Binary classification using ensemble neural networks and interval neutrosophic sets

Aplicação de redes neurais artificiais na identificação de gasolinas adulteradas comercializadas na região de Londrina - Paraná

Evolving Resilient Back-Propagation Algorithm for Energy Efficiency Problem

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