Two recently proposed approaches to recognize temporal patterns have been proposed by Jäger with the so called Echo State Network (ESN) and by Maass with the so called Liquid State Machine (LSM). The ESN approach assumes a sort of “black-box” operability of the networks and claims a broad applicability to several different problems using the same principle. Here we propose a simplified version ...

Accurate wind power prediction is vital for improving grid stability. In order to improve the accuracy of prediction, in this study, a hybrid model combining time-varying filtered empirical modal decomposition (TVFEMD), improved adaptive sparrow search algorithm (IASSA)-optimized phase space reconstruction (PSR) and echo state network (ESN) methods was proposed. First, data were decomposed into...

Fibrillation is characterized by complex spatiotemporal electrical dynamics often driven the continuous creation and annihilation of reentrant waves. While it has been shown that this can be chaotic deterministic, hard to describe its evolution, which could help define for example best times defibrillate using lowest energy. To date impossible predict waves once initiated in space, not only exp...

Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they equipped with very efficient training procedure, Reservoir Computing strategies, such as the ESN, require high-order networks, i.e., many neurons, resulting large number states magnitudes higher than model inputs outputs. A not o...

ارتقای بازدارندگی دفاعی مستلزم تولید گسترده و متنوع محصولات تجهیزات نظامی است که نیازمند به‌کارگیری ظرفیت‌ها قابلیت‌های وسیع صنعتی است. ازآنجاکه تصدی تملک همه این قابلیت‌ها منطقی اقتصادی نیست، بخشی از فعالیت‌های تولیدی در صنایع دفاعی، برون‌سپاری می­‌شود به ایجاد شبکه بزرگی تأمین‌کنندگان منجر شده گستردگی شبکه، تعاملات همکارانه بهبود عملکرد زنجیره تأمین را یک چالش جدی برای مدیران تبدیل کرده سازما...

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent Neural Networks (RNN) are often used to process time series, but only a few training algorithms...

INTRODUCTION Trichomonas vaginalis infection is associated with an increased risk of HIV infection in exposed-seronegative women (ESN) despite their unique immune quiescent profile. It is important to understand possible mechanisms, such as recruitment of activated T cells, by which T. vaginalis could facilitate HIV infection in this population. METHODS We conducted a cross-sectional study ex...

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