Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2017 | 24 | Special Issue S2 |
Tytuł artykułu

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

Warianty tytułu
Języki publikacji
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
Opis fizyczny
  • School of Information Engineering, Wuhan University of Technology,Wuhan, 430074, China
  • School of Information Engineering, Wuhan University of Technology,Wuhan, 430074, China
  • 1. Bhandarkar S M, Wang X: Efficient parallel implementation of the multi-layer perceptron on an SIMD mesh architecture. Neural Parallel & Scientific Computations, Vol. 4, no. 1, pp. 69-82, 1996.
  • 2. Li X J, Li L: IP Core Based Hardware Implementation of Multi-Layer Perceptrons on FPGAs: A Parallel Approach. Advanced Materials Research, Vol. 433, pp.:5647-5653, 2012.
  • 3. Kalaitzakis K, Stavrakakis G S, Anagnostakis E M: Shortterm load forecasting based on artificial neural networks parallel implementation. Electric Power Systems Research, Vol. 63, no. 3, pp.185-196, 2012.
  • 4. Kim Y C, Shanblatt M A: Architecture and statistical model of a pulse-mode digital multilayer neural network. IEEE Transactions on Neural Networks, Vol. 6, no. 5, pp.11091118, 1995.
  • 5. Hikawa H: Frequency-based multilayer neural network with on-chip learning and enhanced neuron characteristics. IEEE Transactions on Neural Networks, Vol. 10, no. 3, pp.:545-53, 1995.
  • 6. Serpen G, Gao Z: Complexity Analysis of Multilayer Perceptron Neural Network Embedded into a Wireless Sensor Network. Procedia Computer Science, Vol. 36, pp.192-197, 2014.
  • 7. Kumar A, Joshi H, P. S: Neural Network Approach for Automatic Landuse Classification of Satellite Images: OneAgainst-Rest and Multi-Class Classifiers. International Journal of Computer Applications, pp.134, 2016.
  • 8. Raza M Q, Khosravi A: A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable & Sustainable Energy Reviews, Vol. 50, pp.1352-1372, 2015.
  • 9. Ahmedalazzawi N: Automatic Recognition System of Infant Cry based on F-Transform. International Journal of Computer Applications, Vol. 102, no. 12, pp.28-32, 2014.
  • 10. Druitt C M, Alici G: Intelligent Control of Electroactive Polymer Actuators Based on Fuzzy and Neurofuzzy Methodologies. Mechatronics IEEE/ASME Transactions on, Vol. 19, no. 6, pp.1951-1962, 2014.
  • 11. Francesquini E, Castro M, Penna P H: On the energy efficiency and performance of irregular application executions on multicore, NUMA and manycore platforms. Journal of Parallel & Distributed Computing, Vol. 76, pp.32-48, 2015.
  • 12. Li Y, Tang X, Cai W: Play Request Dispatching for Efficient Virtual Machine Usage in Cloud Gaming. IEEE Transactions on Circuits & Systems for Video Technology, pp. 1-11, 2015.
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.