Parallel Architecture Training for Neural Networks to Speed up the Process in Multi Input & Output Applications

سال انتشار: 1393
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 754

فایل این مقاله در 21 صفحه با فرمت PDF قابل دریافت می باشد

این مقاله در بخشهای موضوعی زیر دسته بندی شده است:

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

TDCONF01_137

تاریخ نمایه سازی: 19 تیر 1394

چکیده مقاله:

An automatic and optimized approach based on multivariate functions decomposition is presented to face Multi-Input-Multi-Output (MIMO) applications by using Single-Input-Single-Output (SISO) feed-forward Neural Networks (NNs). Indeed, often the learning time and the computational costs are too large for an effective use of MIMO NNs. Since performing a MISO neural model by starting from a single MIMO NN is frequently adopted in literature, the proposed method introduces three other steps: 1) a further decomposition; 2) a learning optimization; 3) a parallel training to speed up the process. Starting from a MISO NN, a collection of SISO NNs can be obtained by means a multidimensional Single Value Decomposition (SVD). Then, a general approach for the learning optimization of SISO NNs is applied. It is based on the observation that the performances of SISO NNs improve in terms of generalization and robustness against noise under suitable learning conditions. Thus, each SISO NN is trained and optimized by using limited training data that allow a significant decrease of computational costs. Moreover, a parallel architecture can be easily implemented. Consequently, the presented approach allows to perform an automatic conversion of MIMO NN into a collection of parallel-optimized SISO NNs. Experimental results will be suitably shown

نویسندگان

Hamzeh Mirzaei

Sama technical and vocational training college, Isalamic Azad University, Shiraz Branch, Shiraz, Iran

Zahra Maghsoodzaeh

Sama technical and vocational training college, Isalamic Azad University, Shiraz Branch, Shiraz, Iran

Razieh Shirdel

Sama technical and vocational training college, Isalamic Azad University, Shiraz Branch, Shiraz, Iran

Hadis Hoseinnia

Sama technical and vocational training college, Isalamic Azad University, Shiraz Branch, Shiraz, Iran

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :
  • K. H. Lim, K. P. Seng, L. M. Ang, S. ...
  • B. Yalcin, K. Ohnishi, "Infinite-Mode Networks for Motion Control", IEEE ...
  • G. Capizzi, S. Coco, C. Giuffrida, A. Laudani, "A neural ...
  • N. Morariu, S. Vlad, "Using Pattern Classification and Recognition Techniques ...
  • N. S. Thomaidis, G. D. Dounias, "A Hybrid Neural Network-Based ...
  • P. Shuang, Y. Wei-kang and G. Mei ling "Application of ...
  • L.O. Fedorovici, R.E. Precup, F. Dragan, R.C. David and . ...
  • L. Jianyo, L. Yongchun, B. Jianpeng, S. Xiaoyun, L. Aihua. ...
  • A. Sun, A. Zhang, Y. Wang., "Largescale Artificial Neural Network ...
  • W. Haikun, D. Weiming, X. Sixin, "Designing Neural Networks Based ...
  • H. Kabir, Y. Wang, M. Yu and Q. J. Zhang, ...
  • F. Riganti Fulginei, A. Salvini, "Neural Network Approach for Modelling ...
  • F. Riganti Fulginei, A. Salvini, C. Coltelli, "A Neuro-Genetc and ...
  • S. Fiori, Singular Value Decomposition Learning O Double Stiefel Manifold, ...
  • H. Trung Huynh, Y. Won, "Training Single Hidden Layer Feedforward ...
  • K. Rohani, M.S. Chen, M. T. Manry, "Neural Subnet Design ...
  • F. Riganti Fulginei, A. Salvini, M. Parodi, "Learning Optimization Of ...
  • B. Curry, "Neural networks and seasonality: Some technicl con siderations", ...
  • E. J. Teoh, K. C. Tan, C. Xiang, "Estimating the ...
  • G. Bebis, M. Georgiopoulos, "Optimal feed-forward neural network architectures", IEEE ...
  • C. Cernazanu, "Training Neural Networks Using Input Data Characteristics, _ ...
  • R. Mirsu, S. Micut, C. Caleanu, D. B. Mirsu, "Optimized ...
  • R. Reed, "Pruning algorithms--A review, " IEEE Trans. Neural Networks, ...
  • T. Kwok, D. Yeung, "Constructive algorithms for structure learning in ...
  • Wolpert D. H., Macready, W. G., "No free lunch theores ...
  • F. Riganti Fulginei, A. Salvini, "Comparative Analysis between Moder Heuristics ...
  • F. Riganti Fulginei, A. Salvini, "Hysteresis model identificatiop by the ...
  • F. Riganti Fulginei, A. Salvini and , Pulcini, "Metric _ ...
  • نمایش کامل مراجع