Reservoir Computing has found many potential applications in the field of complex dynamics. In this article, we explore exceptional capability echo-state network (ESN) model to make it learn a unidirectional coupling scheme from only few time series data system. We show that, once trained with example dynamics drive-response system, machine is able predict response system's for any driver signa...

Plasma concentrations of oestrogen stimulated neurophysin (ESN) were reduced in 28 patients with Alzheimer's disease (AD) compared with 14 age-matched controls, 16 patients with other presenile dementias and 12 patients with major depressive disorder. The ESN response to oestrogen challenge was delayed in 10 AD patients compared with 7 age-matched controls. Reduced basal and oestrogen stimulate...

This technical report is a supplement of our work [ZLW∗] to design bending elasticity by shell thickness. General information and details can be found in [ZLW∗]. In this report, comparison of data interpolation methods and algorithm of conventional ESN-based learning are described in details for further comprehension of the main paper. 1. Comparison of Data Interpolation Learned by Different Me...

The Prognostic and Health Management (PHM) has been developed for more than two decades. It is capable to anticipate the impending failures make decisions in advance extend lifespan of target systems, such as Proton Exchange Membrane Fuel Cell (PEMFC) systems. a critical stage PHM. Among various prognostic methods, data-driven ones could predict system based on device’s knowledge historical dat...

Spiking neural networks are a promising candidate for next-generation machine learning and suitable powerconstrained edge devices. In this paper, we present mixed-signal neuromorphic processor that efficienctly implements an echo state network (ESN) achieves high accuracy without costly on-chip training process. The design employs charge-domain computation circuit efficiently realizes leaky int...

the safety of buried pipes under repeated load has been a challenging task in geotechnical engineering. in this paper artificial neural network and regression model for predicting the vertical deformation of high-density polyethylene (hdpe), small diameter flexible pipes buried in reinforced trenches, which were subjected to repeated loadings to simulate the heavy vehicle loads, are proposed. t...

In an influential paper, Marcus et al. [1999] claimed that connectionist models cannot account for human success at learning tasks that involved generalization of abstract knowledge such as grammatical rules. This claim triggered a heated debate, centered mostly around variants of the Simple Recurrent Network model [Elman, 1990]. In our work, we revisit this unresolved debate and analyze the un...

with the increase of the volume of information and the progress in technology, the deficiency of traditional algorithms for fast information retrieval becomes more clear. when large volumes of data are to be handled, the use of neural network as an artificial intelligent technique is a suitable method to increase the information retrieval speed. neural networks present a suitable representation...

Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesirable phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...