Temporal phasing analysis is integral to ubiquitous/“smart” coaching devices and sport science. This study presents a novel approach to autonomous temporal phasing of human motion from captured tennis activity (3D data, 66 time-series). Compared to the optimised Echo State Network (ESN) model achieving 85 % classification accuracy, the ESN ensemble system demonstrates improved classification of...

A new class of state-space models, reservoir models, with a fixed state transition structure (the "reservoir") and an adaptable readout from the state space, has recently emerged as a way for time series processing and modeling. Echo state network (ESN) is one of the simplest, yet powerful, reservoir models. ESN models are generally constructed in a randomized manner. In our previous study (Rod...

Recurrent neural networks (RNNs) with rich feature vectors of past values can provide accurate point forecasts for series that exhibit complex serial dependence. We propose two approaches to constructing deep time probabilistic models based on a variant RNN called an echo state network (ESN). The first is where the output layer ESN has stochastic disturbances and shrinkage prior additional regu...

PURPOSE To investigate the efficiency of electrical stimulation in the muscle maintenance and nerve regeneration after end-to-side neurorrhaphy (ESN). METHODS Sixty male Wistar rats (Rattus norvegicus) were divided into four experimental groups. Control group (Control), Denervated Group (Denervated); Group with End-to-side neurorrhaphy (ESN); Group with End-to-side neurorrhaphy and electrical...

Enterprise social networking (ESN) is widely used to facilitate employees’ communication and collaboration in today’s competitive business environment. However, the underlying mechanism through which ESN improves team performance is not clear. To address this gap, we propose that the ESN use may affect the building of transactive memory systems (TMS), which in turn influence team performance. S...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design pr...

Enterprise social networking (ESN) is a relatively new phenomenon and not yet fully understood. An important reason for this lies in the fact that ESN are built on malleable technologies that do not lend themselves to immediate forms of workplace usage determined or prescribed by their features. Rather, their potential only manifests when people make sense of and incorporate them into their day...