Estimating the Number of Hidden Neurons in a Feedforward Network Using the Singular Value Decomposition

Teoh, E.J.; Chen, Xiang; Tan, Kay Chen

doi:10.1007/11759966_126

Cited by 22 publications

(15 citation statements)

References 12 publications

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“…Different training errors can be obtained by BP neural network training. Among them, we let different numbers of hidden nodes as the dynamic parameters of BP neural networks [40][41][42] and select the smallest simulation error of the BP neural network as a forecasting model of petroleum projects. Then, we will confirm Fig.…”

Section: The Design Of Bp Neural Networkmentioning

confidence: 99%

Design and analysis of a general data evaluation system based on social networks

Mao

et al. 2018

J Wireless Com Network

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A social network is a social structure with a set of social actors and other social interactions between actors. The social network supports a set of methods to explain the structure of whole social entities as well as a variety of theories in these structures. The study of these structures uses social network analysis to explain local and global patterns and examine network dynamics. The related economic data is important for our economic developments. We could analyze the economic data to explore the investments. In this paper, we focus on the design and analysis of automatic management systems to make a decision about the petroleum projects. Petroleum is the lifeblood of economic development. So, we need an automatic and effective management system to make the mining investments of petroleum. Before deciding to invest in the petroleum project, the engineers will describe this project by providing the sufficient economic data. Based on this data, the decision can undertake professional analysis, to determine whether the project is feasible. To automate the manual process and overcome the traditional evaluation method, the BP (back propagation) neural network is applied in a petroleum project economic evaluation. This method opens up a new way for the evaluation of petroleum projects. In this article, we first introduce the application of BP neural network. Then, we introduce the principle and shortcoming of BP neural network. Based on it, we optimize the BP neural network. Secondly, according to the characteristics of petroleum projects, we obtain the economic evaluation indicators about petroleum projects, which are used as the input of the BP neural network. Thirdly, we propose the design of the BP neural network and select the smallest simulation error of the BP neural network as a forecasting model of petroleum projects. In order to improve the accuracy, the labeled training samples are selected for BP neural network training. Finally, based on extensive simulation experimental results, we demonstrate our scheme about the BP neural network of petroleum project evaluation is effective and has overperformance than traditional schemes in terms of convenience and accurate decision-making suggestions.

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Section: The Design Of Bp Neural Networkmentioning

confidence: 99%

Design and analysis of a general data evaluation system based on social networks

Mao

et al. 2018

J Wireless Com Network

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“…The number of hidden layer units has been shown to be an important parameter for data learning and accurate identification. Teoh et al (2006) attempt to quantify the effect of increasing the number of neurons in the hidden layer in a feedforward neural network using singular value decomposition (SVD). Shuxiang and Ling (2008) reviewed several mechanisms in the neural network literature that have been used to determine the optimal number of hidden layer neurons (given an application), and proposed a new approach based on some mathematical evidence and applied it to financial data mining.…”

Section: Optimization Of the Number Of Neurons In The Hidden Layermentioning

confidence: 99%

Estrus Detection in Sows Based on Texture Analysis of Pudendal Images and Neural Network Analysis

Seo

Min

Kim

et al. 2012

Journal of Biosystems Engineering

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Worldwide trends in animal welfare have resulted in an increased interest in individual management of sows housed in groups within hog barns. Estrus detection has been shown to be one of the greatest determinants of sow productivity. Purpose: We conducted this study to develop a method that can automatically detect the estrus state of a sow by selecting optimal texture parameters from images of a sow's pudendum and by optimizing the number of neurons in the hidden layer of an artificial neural network. Methods: Texture parameters were analyzed according to changes in a sow's pudendum in estrus such as mucus secretion and expansion. Of the texture parameters, eight gray level co-occurrence matrix (GLCM) parameters were used for image analysis. The image states were classified into ten grades for each GLCM parameter, and an artificial neural network was formed using the values for each grade as inputs to discriminate the estrus state of sows. The number of hidden layer neurons in the artificial neural network is an important parameter in neural network design. Therefore, we determined the optimal number of hidden layer units using a trial and error method while increasing the number of neurons. Results: Fifteen hidden layers were determined to be optimal for use in the artificial neural network designed in this study. Thirty images of 10 sows were used for learning, and then 30 different images of 10 sows were used for verification. Conclusions: For learning, the back propagation neural network (BPN) algorithm was used to successful estimate six texture parameters (homogeneity, angular second moment, energy, maximum probability, entropy, and GLCM correlation). Based on the verification results, homogeneity was determined to be the most important texture parameter, and resulted in an estrus detection rate of 70%.

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“…The number of neurons in the hidden layer is chosen to be the number of input neurons. Optimum selection of the number of hidden layer neurons is explored in [33].…”

Section: Proposed Back-propagation Artificial Neural Network Modelmentioning

confidence: 99%

Back-Propagation Artificial Neural Network for ERP Adoption Cost Estimation

Kotb

Haddara

Kotb

2011

Communications in Computer and Information Science

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Abstract. Small and medium size enterprises (SMEs) are greatly affected by cost escalations and overruns Reliable cost factors estimation and management is a key for the success of Enterprise Resource Planning (ERP) systems adoptions in enterprises generally and SMEs specifically. This research area is still immature and needs a considerable amount of research to seek solid and realistic cost factors estimation. Majority of research in this area targets the enhancement of estimates calculated by COCOMO family models. This research is the beginning of a series of models that would try to replace COCOMO with other models that could be more adequate and focused on ERP adoptions. This paper introduces a feedforward back propagation artificial neural network model for cost factors estimation. We comment on results, merits and limitations of the model proposed. Although the model addresses SMEs, however, it could be extended and applied in various environments and contexts.

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Estimating the Number of Hidden Neurons in a Feedforward Network Using the Singular Value Decomposition

Cited by 22 publications

References 12 publications

Design and analysis of a general data evaluation system based on social networks

Design and analysis of a general data evaluation system based on social networks

Estrus Detection in Sows Based on Texture Analysis of Pudendal Images and Neural Network Analysis

Back-Propagation Artificial Neural Network for ERP Adoption Cost Estimation

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