Hardware-Based Framework of Photonic Reservoir Computing with Coupled SOAs Network

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Louiza Dehyadegari ◽  
Mohammad Reza Salehi ◽  
Maryam Sedigh Sarvestani ◽  
Ebrahim Abiri
Keyword(s):  
Time Series ◽  
Random Sequence ◽  
Reservoir Computing ◽  
Time Series Classification ◽  
Noisy Signal ◽  
Photonic Structure ◽  
New Approach ◽  
Triangular Wave ◽  
Wave Forms ◽  
One Step

AbstractIn this paper, a photonic structure for reservoir computing is presented. A new approach for photonic reservoir computing is proposed using a network of SOAs arranged in a waterfall topology and coupled by semi-transparent mirrors. The proposed method is then simulated in OptiSystem software. As this software is hardware framework-based, the simulation result is one step closer to fabrication than the previous works. A series of noisy and noise-free time-series signals are employed to evaluate the performance of the proposed method. The used time-series signals contain random sequence of both square and triangular wave forms. The results of this simulation show 92.14% recognition of a noise-free signal and 79.32% of a 60 dB noisy signal. The parameters of the simulated photonic reservoir network are also optimized to achieve higher accuracy in this time-series classification.

scholarly journals FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Miquel L. Alomar ◽  
Vincent Canals ◽  
Nicolas Perez-Mora ◽  
Víctor Martínez-Moll ◽  
Josep L. Rosselló
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Power Dissipation ◽  
Hardware Implementation ◽  
Functional System ◽  
Time Series Forecasting ◽  
Reservoir Computing ◽  
New Approach ◽  
Echo State Networks ◽  
Learning Capabilities

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting.

A Frequent Pattern Based Time Series Classification Framework

2010 ◽  
Vol 32 (2) ◽  
pp. 261-266
Author(s):  
Li Wan ◽  
Jian-xin Liao ◽  
Xiao-min Zhu ◽  
Ping Ni
Keyword(s):  
Time Series ◽  
Frequent Pattern ◽  
Time Series Classification ◽  
Classification Framework

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

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