Rough Neural Network of Variable Precision

Liu, Hongjian; Tuo, Hongya; Liu, Yuncai

doi:10.1023/b:nepl.0000016851.47914.40

Cited by 16 publications

(9 citation statements)

References 14 publications

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“…By means of Lagrange multipliers, a new objective function (13) can be constructed, where original objective function (9) and constraint condition (10) are integrated.…”

Section: Gaustafson-kessel Fuzzy Clustering Algorithmmentioning

confidence: 99%

“…By setting the gradients of J (X, U, V ) with respect to U, V and λ to zero, (13) can be minimized if…”

Section: Gaustafson-kessel Fuzzy Clustering Algorithmmentioning

confidence: 99%

“…In experiment, rough logic neural network (RLNN) introduced in Ref. [13,14] is adopted to compare with our proposed rough neural network. A small part (30%) of total samples is selected as training samples, while the rest (70%) are used for testing.…”

Section: Comparison With Conventional Rough Logic Neural Networkmentioning

confidence: 99%

“…Due to the statistical learning characteristic and self adaptability, neural network has strong generalization and fault-tolerant ability. In past decade, many rough neural networks with different integration ways have been studied [10], among them, rough logic neural network (RLNN) is extensively adopted [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

Zhang

Wang

2009

Neural Process Lett

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Based on bottom-up fuzzy rough data analysis, a new rough neural network decision-making model is proposed. Through supervised Gaustafason-Kessel (G-K) clustering algorithm, proper fuzzy clusters are found to partition the input data space. At the same time cluster number is searched by monotone increasing process. If the cluster number matches with that exactly exist in data sets then excellent fuzzy rough data modeling (FRDM) model can be built. And by integrating it with neural network technique, corresponding rough neural network is constructed. Our method overcomes the defects of conventional top-down based rough logic neural network (RLNN) method, and it also achieves adaptive learning ability and comprehensive soft decision-making ability compared with FRDM model. The experiment results indicate that our method has stronger generalization ability and more compact network structure than conventional RLNN.

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“…By means of Lagrange multipliers, a new objective function (13) can be constructed, where original objective function (9) and constraint condition (10) are integrated.…”

Section: Gaustafson-kessel Fuzzy Clustering Algorithmmentioning

confidence: 99%

“…By setting the gradients of J (X, U, V ) with respect to U, V and λ to zero, (13) can be minimized if…”

Section: Gaustafson-kessel Fuzzy Clustering Algorithmmentioning

confidence: 99%

Section: Comparison With Conventional Rough Logic Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

Zhang

Wang

2009

Neural Process Lett

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“…We will use a K equal to 6 for our next tests. For more information about rough neural network classifier and rough neural fuzzy network classifier use these references [3,4,5].…”

Section: IImentioning

confidence: 99%

The Comparison of Different Classifiers for Precision Improvement in Image Retrieval

Lotfabadi¹,

Mahmoudie

2010

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems

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In many researches, valuable studies have been done for feature extraction from images data-base, but because of weak classifiers using, good results have not been achieved. In this paper, different classifiers are compared in order to increase image retrieval system precision. Five different classifiers are used in the paper: the support vector-machine, the MLP neural network, the K-nearest neighbor, the rough neural network, and the rough fuzzy neural network. The rough fuzzy neural network and the rough neural network have not been used in image retrieval implication up to now. The innovation of this research is the using of these classifiers in the image retrieval implication. From the performed test, it is concluded that the rough fuzzy neural network classifier has performed better than other classifiers and increased the image retrieval precision. The COREL image data-base with 1000 images in ten content groups has been used and the classifiers have been compared.

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Application of Variable Precision Rough Set Model and Neural Network to Rotating Machinery Fault Diagnosis

Zhou

Yin

2005

Lecture Notes in Computer Science

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Rough Neural Network of Variable Precision

Cited by 16 publications

References 14 publications

Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

The Comparison of Different Classifiers for Precision Improvement in Image Retrieval

Application of Variable Precision Rough Set Model and Neural Network to Rotating Machinery Fault Diagnosis

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