An information-processing paradigm in the brain is proposed, instantiated in an artificial neural network using biologically motivated temporal encoding. The network will locate within the external world stimulus, the target memory, defined by a specific pattern of micro-features. The proposed network is robust and efficient. Akin in operation to the Swarm Intelligence paradigm, Stochastic Diff...

In this paper, a learning algorithm combining memory-less learning and memory-based learning is proposed for agents operating under POMDP. In the first stage of the proposed algorithm, memory-less learning is applied. The stochastic gradient method is employed as a memory-less learning algorithm. In the first stage, a state-action set series that accomplishes the task is stored in memory. In th...

Abstract The origin of switching parameter variations in metal-oxide resistive-switching random access memory (RRAM) is studied. The stochastic formation/rupture of the conductive filaments (CFs) is modeled and incorporated with a trap-assisted-tunneling (TAT) current solver. The experimental DC I-V characteristics and pulse transient waveform featuring the current fluctuation during the reset ...

This paper introduces a family of “generalized long-memory time series models”, in which observations have a specified conditional distribution, given a latent Gaussian fractionally integrated autoregressive moving average (ARFIMA) process. The observations may have discrete or continuous distributions (or a mixture of both). The family includes existing models such as ARFIMA models themselves,...

in this paper, a class of semi-implicit two-stage stochastic runge-kutta methods (srks) of strong global order one, with minimum principal error constants are given. these methods are applied to solve itô stochastic differential equations (sdes) with a wiener process. the efficiency of this method with respect to explicit two-stage itô runge-kutta methods (irks), it method, milstien method, sem...

In the paper we study stochastic convolution appearing in Volterra equation driven by so called Lévy process. By Lévy process we mean a process with homogeneous independent increments, continuous in probability and cadlag.

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain efficient estimates of...

http://dx.doi.org/10.1016/j.ejor.2014.03.045 0377-2217/ 2014 Elsevier B.V. All rights reserved. ⇑ Corresponding author at: Department of Statistical Sciences, University of Cape Town, Rondebosch 7701, South Africa. Tel.: +27 21 6505058; fax: +27 21 6504773. E-mail addresses: [email protected] (I. Durbach), [email protected] (R. Lahdelma), [email protected] (P. Salminen). Ian Dur...