Dynamic Structural Neural Network
dc.cclicence | CC-BY-NC | en |
dc.contributor.author | Chiclana, Francisco | en |
dc.contributor.author | Cu Nguyen, Giap | en |
dc.contributor.author | Le Hoang, Son | en |
dc.date.acceptance | 2017-12-12 | en |
dc.date.accessioned | 2017-12-19T09:31:53Z | |
dc.date.available | 2017-12-19T09:31:53Z | |
dc.date.issued | 2017-12 | |
dc.description | The file attached to this record is the author's final peer reviewed version. | en |
dc.description.abstract | Artificial neural network (ANN) has been well applied in pattern recognition, classification and machine learning thanks to its high performance. Most ANNs are designed by a static structure whose weights are trained during a learning process by supervised or unsupervised methods. These training methods require a set of initial weights values, which are normally randomly generated, with different initial sets of weight values leading to different convergent ANNs for the same training set. Dealing with these drawbacks, a trend of dynamic ANN was invoked in the past year. However, they are either too complex or far from practical applications such as in the pathology predictor in binary multi-input multi-output (MIMO) problems, when the role of a symptom is considered as an agent, a pathology predictor’s outcome is formed by action of active agents while other agents’ activities seem to be ignored or have mirror effects. In this paper, we propose a new dynamic structural ANN for MIMO problems based on the dependency graph, which gives clear cause and result relationships between inputs and outputs. The new ANN has the dynamic structure of hidden layer as a directed graph showing the relation between input, hidden and output nodes. The properties of the new dynamic structural ANN are experienced with a pathology problem and its learning methods’ performances are compared on a real well known dataset. The result shows that both approaches for structural learning process improve the quality of ANNs during learning iteration. | en |
dc.explorer.multimedia | No | en |
dc.funder | This research is funded by Graduate University of Science and Technology under grant number GUST.STS.ÐT2017- TT02. The authors are grateful for the support from the Institute of Information Technology, Vietnam Academy of Science and Technology. We received the necessary devices as experiment tools to implement proposed method. | en |
dc.identifier.citation | Cu Nguyen, G., Le Hoang, S., Chiclana, F. (2017) Dynamic structural neural network. Journal of Intelligent & Fuzzy Systems, 34(4), pp.2479–2490. | en |
dc.identifier.doi | https://doi.org/10.3233/jifs-171947 | |
dc.identifier.issn | 1064-1246 | |
dc.identifier.issn | 1875-8967 | |
dc.identifier.uri | http://hdl.handle.net/2086/15018 | |
dc.language.iso | en_US | en |
dc.peerreviewed | Yes | en |
dc.projectid | This research is funded by Graduate University of Science and Technology under grant number GUST.STS.ÐT2017- TT02. The authors are grateful for the support from the Institute of Information Technology, Vietnam Academy of Science and Technology. We received the necessary devices as experiment tools to implement proposed method. | en |
dc.publisher | IOS Press | en |
dc.researchgroup | Centre for Computational Intelligence | en |
dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
dc.subject | Artificial neural network | en |
dc.subject | Binary multi-input multi-output problems | en |
dc.subject | Dynamic structure | en |
dc.subject | Genetic algorithm | en |
dc.subject | Greedy algorithm | en |
dc.subject | Medical diagnosis | en |
dc.title | Dynamic Structural Neural Network | en |
dc.type | Article | en |
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