Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment

Ser, Javier Del; Laña, Ibai; Manibardo, Eric L.; Oregi, Izaskun; Osaba, Eneko; Lobo, Jesús L.; Bilbao, Miren Nekane; Vlahogianni, Eleni I.

doi:10.1109/itsc45102.2020.9294200

Cited by 12 publications

(2 citation statements)

References 30 publications

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“…Due to the aforementioned advantages, RC has attracted more and more attentions in research community. It has be utilized in signal equalization [67,[75][76][77][78][79][80][81], speech recognition [82,83], time-series prediction or classification [82,[84][85][86][87][88][89][90][91], and de-noising in temporal sequence [92,93].…”

Section: Optical Reservoir Computingmentioning

confidence: 99%

The challenges of modern computing and new opportunities for optics

Zhang

et al. 2021

PhotoniX

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In recent years, the explosive development of artificial intelligence implementing by artificial neural networks (ANNs) creates inconceivable demands for computing hardware. However, conventional computing hardware based on electronic transistor and von Neumann architecture cannot satisfy such an inconceivable demand due to the unsustainability of Moore’s Law and the failure of Dennard’s scaling rules. Fortunately, analog optical computing offers an alternative way to release unprecedented computational capability to accelerate varies computing drained tasks. In this article, the challenges of the modern computing technologies and potential solutions are briefly explained in Chapter 1. In Chapter 2, the latest research progresses of analog optical computing are separated into three directions: vector/matrix manipulation, reservoir computing and photonic Ising machine. Each direction has been explicitly summarized and discussed. The last chapter explains the prospects and the new challenges of analog optical computing.

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Section: Optical Reservoir Computingmentioning

confidence: 99%

The challenges of modern computing and new opportunities for optics

Zhang

et al. 2021

PhotoniX

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“…In [45], based on time series analysis, a serially connected multi-reservoir echo state network (MR-ESN) is developed to conduct the prediction of solar irradiance, which has the advantages of an ESN, such as high efficiency. In short-term traffic prediction, the DeepESN is used to predict the future values of the traffic parameters of interest accurately [46]. Despite the proven ability of DeepESNs' ability to address time-series data across various domains, their application in the realm of UWOC remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

A Deep Echo State Network-Based Novel Signal Processing Approach for Underwater Wireless Optical Communication System with PAM and OFDM Signals

et al. 2023

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Underwater wireless optical communication (UWOC) plays key role in the underwater wireless sensor networks (UWSNs), which have been widely employed for both scientific and commercial applications. UWOC offers high transmission data rates, high security, and low latency communication between nodes in UWSNs. However, significant absorption and scattering loss in underwater channels, due to ocean water conditions, can introduce highly non-linear distortion in the received signals, which can severely deteriorate communication quality. Consequently, addressing the challenge of processing UWOC signals with low optical signal-to-noise ratios (OSNRs) is critical for UWOC systems. Increasing the transmitting optical power and investigating more advanced signal processing technologies to recover transmitted symbols are two primary approaches to improve system tolerance in noisy UWOC signal channels. In this paper, we propose and demonstrate the application of deep echo state networks (DeepESNs) for channel equalization in high-speed UWOC systems to enhance system performance with both PAM and QPSK-OFDM modulations. Our experimental results demonstrate the effectiveness of DeepESNs in UWOC systems, achieving error-free underwater transmission over 40.5 m with data rates up to 167 Mbps. Moreover, we compare the performance of DeepESNs to conventional echo state networks and provide suggestions on the configuration of a DeepESN for UWOC signals.

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Deep Reservoir Computing

Gallicchio

Micheli

2021

Natural Computing Series

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This chapter surveys the recent advancements on the extension of Reservoir Computing toward deep architectures, which is gaining increasing research attention in the neural networks community. Within this context, we focus on describing the major features of Deep Echo State Networks based on hierarchical composition of multiple reservoirs. The intent is to provide a useful reference to guide applications and further developments of this efficient and effective class of approaches to deal with times-series and more complex data within a unified description and analysis.

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Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment

Cited by 12 publications

References 30 publications

The challenges of modern computing and new opportunities for optics

The challenges of modern computing and new opportunities for optics

A Deep Echo State Network-Based Novel Signal Processing Approach for Underwater Wireless Optical Communication System with PAM and OFDM Signals

Deep Reservoir Computing

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