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Kaikhah, Khosrow; Garlick, Ryan

doi:10.1023/a:1008315023738

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…Therefore the first step to make in research like this is to locate the best ANN strategy for each problem. The more widespread approaches of machine learning included in this work are: MultiLayer Perceptron (MLP) [13,35], Radial Basis Function (RBF) [43,55], Support Vector Machines (SVM) [18,68], Recurrent Neural Networks (RNN) [33,50], Jordan-Elman Networks (ELN) [38,39] and Self Organizing Feature Maps (SOFM) [15,41]. Moreover, one interesting way to increase the performance of an ANN is to minimize the cost function during the training phase.…”

Section: Introductionmentioning

confidence: 99%

An optimization methodology for machine learning strategies and regression problems in ballistic impact scenarios

et al. 2010

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In domains with limited data, such as ballistic impact, prior researches have proven that the optimization of artificial neural models is an efficient tool for improving the performance of a classifier based on MultiLayer Perceptron. In addition, this research aims to explore, in the ballistic domain, the optimization of other machine learning strategies and their application in regression problems. Therefore, this paper presents an optimization methodology to use with several approaches of machine learning in regression problems, maximizing the limited dataset and locating the best network topology and input vector of each network model. This methodology is tested in real regression scenarios of ballistic impact with different artificial neural models, obtaining substantial improvement in all the experiments. Furthermore, the quality stage, based on criteria of information theory, enables the determination of when the complexity of the network design does not penalize the fit over the data and thereby the selection of the best neural network model from a series of candidates. Finally, the results obtained show the relevance of this methodology and its application improves the performance and efficiency of multiple machine learning strategies in regression scenarios.

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Section: Introductionmentioning

confidence: 99%

An optimization methodology for machine learning strategies and regression problems in ballistic impact scenarios

et al. 2010

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A Reliable Small Sample Classification Algorithm by Elman Neural Network Based on PLS and GA

et al. 2019

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Aiming at the small sample with high-feature dimension and few numbers will cause a serious problem if simply using the traditional Elman neural network to deal with the small sample; these problems include poor learning ability, the redundancy structure, and incomplete training; these defects will result in lower operating efficiency and poor recognition precision. In this paper, combining the theory of partial least squares (PLS) and genetic algorithm (GA), as well as the nature of Elman neural network, and an optimized Elman neural network classification algorithm based on PLS and GA (PLS-GA-Elman) is established. The new algorithm reduces the feature dimension of small samples by PLS, the relatively ideal lowdimensional data is obtained, and the purpose reduces the neural network's inputs and simplifies its structure. Using GA to optimize the connection weights, threshold values, and the number of hidden neurons and adopting the optimized way of encoding respectively and evolving simultaneously can improve the neural network incomplete training condition, leading to fewer number of samples and improvement in training speed and generalization ability; this ensures the optimal Elman neural network algorithm. A new algorithm based on twice consecutive optimization was the basis for a precise classification model. The results of experimental analysis illustrate that operating efficiency and classification precision of the new algorithm have been improved.

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Simultaneous optimization of artificial neural networks for financial forecasting

Kim

Ahn

2011

Appl Intell

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Untitled

Cited by 8 publications

References 6 publications

An optimization methodology for machine learning strategies and regression problems in ballistic impact scenarios

An optimization methodology for machine learning strategies and regression problems in ballistic impact scenarios

A Reliable Small Sample Classification Algorithm by Elman Neural Network Based on PLS and GA

Simultaneous optimization of artificial neural networks for financial forecasting

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