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The iterative learning control (ILC) is constructed for the discrete-time networked nonlinear systems with random measurement losses and unknown control direction. The random packet loss is modeled by an arbitrary stochastic sequence with bounded length requirement. A novel regulating approach based on truncations is introduced to make the proposed algorithm find the correct control direction adaptively...
The Euler-Maruyama method is applied to a simple stochastic differential equation (SDE) with discontinuous drift. Convergence aspects are investigated in the case, where the Euler-Maruyama method is simulated in dyadic points. A strong rate of convergence is presented for the numerical simulations and it is shown that the produced sequences converge almost surely. This is an improvement of the general...
Variational inequality problems find wide applicability in modeling a range of optimization and equilibrium problems. We consider the stochastic generalization of such a problem wherein the mapping is pseudomonotone and make two sets of contributions in this paper. First, we provide sufficiency conditions for the solvability of such problems that do not require evaluating the expectation. Second,...
For a class of stochastic hybrid systems, we characterize the sets to which bounded solutions converge. We show that each sample path converges to a weakly totally recurrent in probability set. This characterization is often tighter than the usual assertion that a solution converges to a weakly invariant set. Consequently, the results here can be viewed as a generalization of the invariance principle,...
In this tutorial paper a brief exposition is made for the research area related to development and justification of the so called method of continuous models (MNM). The essence of the method is in replacement of the analysis or design problem for a discrete stochastic system with a similar problem for its simplified (averaged) continuous-time model. Continuous-time models described by either ordinary...
Robust SLAM methods can allow robots to recover correct maps even in the presence of incorrect loop closures. While these approaches improve robustness to outliers, they are susceptible to getting caught in local minima, a problem which is exacerbated by poor initial estimates. In this paper, we describe a stochastic gradient descent optimization approach that exhibits greater robustness to poor initial...
The iterative learning control (ILC) is constructed for the discrete-time stochastic systems with random measurement losses modeled by a stochastic sequence. A simple P-type update law is used and the almost sure convergence is strictly proved for both linear case and nonlinear case based on stochastic approximation. Illustrative examples show the effectiveness of the proposed approach.
This paper considers the problems of distributed online prediction and optimization. Each node in a network of processors processes a stream of data in an online manner. Before the next data point arrives, the processor must make a prediction. Then, after receiving the next point, the processor accrues some loss or regret. The goal of the processors is to minimize the total aggregate regret. We propose...
In this paper we are concerned with a class of stochastic multicommodity network flow problems, the so called capacity expansion planning problems. We consider a two-stage stochastic optimization formulation that incorporates uncertainty in the problem parameters. To address the computational complexity of these stochastic models, we propose a decomposition method to divide the original problem into...
We consider a stochastic variational inequality (SVI) problem with a continuous and monotone mapping over a compact and convex set. Traditionally, stochastic approximation (SA) schemes for SVIs have relied on strong monotonicity and Lipschitzian properties of the underlying map. We present a regularized smoothedSA(RSSA) schemewherein the stepsize, smoothing, and regularization parametersare diminishing...
Distributed reinforcement learning algorithms for collaborative multi-agent Markov decision processes (MDPs) are presented and analyzed. The networked setup consists of a collection of agents (learners) which respond differently (depending on their instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. With the objective of jointly learning...
This paper studies recursive nonlinear least squares parameter estimation in inference networks with observations distributed across multiple agents and sensed sequentially over time. Conforming to a given inter-agent communication or interaction topology, distributed recursive estimators of the consensus + innovations type are presented in which at every observation sampling epoch the network agents...
In this paper, a relevant document retrieval method is proposed for document retrieval systems with vector space models (VSM). In recent years, with the size of the database becomes extremely large, there becomes a high demanding of an accurate and fast-time document retrieval algorithm. Based on the maximum similarity criterion, a document retrieval algorithm using the discrete stochastic optimization...
We consider the solution of a stochastic convex optimization problem E[f(x;θ∗,ξ)] in x over a closed and convex set X in a regime where θ∗ is unavailable. Instead, θ∗ may be learnt by minimizing a suitable metric E[g(θη)] in θ over a closed and convex set Θ. We present a coupled stochastic approximation scheme for the associated stochastic optimization problem with imperfect information. The schemes...
This paper considers a cross-layer optimization problem driven by multi-timescale stochastic exogenous processes in wireless communication networks. Due to the hierarchical information structure in a wireless network, a mixed timescale stochastic iterative algorithm is proposed to track the time-varying optimal solution of the cross-layer optimization problem, where the variables are partitioned into...
It is very important for the future wireless or mobile communication systems to achieve high speed Turbo decoder. A novel Turbo decoder structure and implementation scheme are proposed in this paper. The MAX-log-MAP algorithm is implemented in probability domain with stochastic computation method in this paper. The signed stochastic calculations are adopted to meet the requirements of log-MAP decoder...
The aim of this paper is to enforce stochastic techniques in a classical ElectroMagnetic Compatibility (EMC) issue. An experimental setup has been developed ex nihilo to model it. First, a quick glance over the physical context will give precisions about the objectives of this work. Then, the statistical developments are derived both from classical (but costly) Monte Carlo (MC) technique and other...
Due to the high demand of spectrum utilization, cognitive radio (CR) network has been a promising solution to the problem of spectrum scarcity by using dynamic spectrum access technique. In this paper, we study one of the CR network architectures where the CR base stations (CRBSs) demand spectrum resources for the CR users to directly access and utilize. We applied an economical Cournot Game model...
This paper introduces an inducing strategy for cooperation into prisoner's dilemma games. Based on the bounded rationality of players, using stochastic reactive strategies in games, we show that the inducing strategy have the ability to enhance both cooperation frequencies and payoffs of players.
Our goal is to study numerical approximations of the solutions of backward stochastic differential equations in some general conditions for the coefficient functions and without the condition of the continuity for the final data. An example which sustains our explicitly scheme for solving a class of this stochastic differential equation is presented.
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