Fuzzy method and neural network model parallel implementation of multi-layer neural network based on cloud computing for real time data transmission in large offshore platform

• Autorzy: Zhang H.. , Qin W.

Identyfikatory

DOI

10.1515/pomr-2017-0062

• Języki publikacji: EN

Abstrakty

EN

With the rapid development of electronic technology, network technology and cloud computing technology, the current data is increasing in the way of mass, has entered the era of big data. Based on cloud computing clusters, this paper proposes a novel method of parallel implementation of multilayered neural networks based on Map-Reduce. Namely in order to meet the requirements of big data processing, this paper presents an efficient mapping scheme for a fully connected multi-layered neural network, which is trained by using error back propagation (BP) algorithm based on Map-Reduce on cloud computing clusters (MRBP). The batch-training (or epoch-training) regimes are used by effective segmentation of samples on the clusters, and are adopted in the separated training method, weight summary to achieve convergence by iterating. For a parallel BP algorithm on the clusters and a serial BP algorithm on uniprocessor, the required time for implementing the algorithms is derived. The performance parameters, such as speed-up, optimal number and minimum of data nodes are evaluated for the parallel BP algorithm on the clusters. Experiment results demonstrate that the proposed parallel BP algorithm in this paper has better speed-up, faster convergence rate, less iterations than that of the existed algorithms.

Słowa kluczowe

EN

parallel implementation; multi-layer neural network; cloud computing

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2017
• Tom: S 2
• Strony: 39--44
• Opis fizyczny: Bibliogr. 12 poz., rys.

Twórcy

autor

Zhang H..

• School of Information Engineering Wuhan University of Technology Wuhan Hubei, 430074 China, tel.: [phone]

autor

Qin W.

• School of Information Engineering, Wuhan University of Technology,Wuhan, 430074, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).