Abstract. The convergence of Monte Carlo method for numerical integration can often be improved by replacing pseudorandom numbers (PRNs) with more uniformly distributed numbers kno...
In this paper we discuss the design of optimization algorithms for cognitive wireless networks (CWNs). Maximizing the perceived network performance towards applications by selectin...
We consider iterative algorithms of the form z := f(z), executed by a parallel or distributed computing system. We focus on asynchronous implementations whereby each processor ite...
In this paper we derive convergence rates for Q-learning. We show an interesting relationship between the convergence rate and the learning rate used in Q-learning. For a polynomi...
Abstract. In this paper, a method of improving the learning time and convergence rate is proposed to exploit the advantages of artificial neural networks and fuzzy theory to neuron...
Kwang-Baek Kim, Sungshin Kim, Young Hoon Joo, Am S...
—The backpropagation algorithm is a very popular approach to learning in feed-forward multi-layer perceptron networks. However, in many scenarios the time required to adequately ...
— This paper addresses learning based adaptive resource allocation for wireless MIMO channels with Markovian fading. The problem is posed as Constrained Markov Decision Process w...
Abstract. The purpose of this paper is to comment a frequent observation by the engineers studying acoustic scattering. It is related to the convergence of the GMRES method when so...
In rendering a virtual sound over two loudspeakers, adaptive inverse filtering is required for crosstalk cancellation. Although various adaptive algorithms have been proposed for...
Abstract—This paper proposes a novel discrete time secondorder distributed consensus time synchronization (SO-DCTS) algorithm for wireless sensor networks. The consensus properti...