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In the last few decades, many opinion formation models have been proposed to describe how opinion interactions among individuals result in different distributions of opinions within social systems. Emotion plays a key role when people try to influence others' opinions, but applying emotion to opinion formation models has attracted little attention. In this paper, we discuss how emotion can affect...
Stochastic optimization is playing an increasingly important in machine learning in the big data era. In this paper, we use forward-backward splitting for the stochastic optimization problems, where the objective is the sum of two functions: one is the expected risk function, another is a regularized term. At each iteration of this method, we just use a single sample to adjust the variables. We prove...
In this paper, the asymptotic properties are addressed for a class of discretely observed nonlinear nonhomogeneous stochastic system with unknown parameter. The local asymptotic normality is derived based on the discrete observation of the approximate maximum likelihood estimator of the unknown parameter in the drift term. The purpose of the addressed problem is to analyze the weak convergence of...
This paper presents an optimal Day-Ahead Electricity Market (DAM) bidding strategy for an aggregator leveraging a pool of residential prosumers: residential customers with local photovoltaic (PV) production and plug-in electric vehicle (PEV) charging flexibility. The aggregator's point-of-view differs from the social planner angle that is taken in the majority of the existing literature, mainly the...
Stochastic turbo decoder is a new scheme for turbo codes. But the long decoding latency and high complexity are two main challenges for fully parallel stochastic turbo decoders. In this paper, we proposed a novel stochastic turbo decoder scheme with two high accuracy stochastic operator modules, including no-scaling stochastic addition and stochastic normalization operator, which can improve the decoding...
Distributed and cooperative algorithms are of preponderant importance for the correct operation of multiagent systems. In particular, average consensus algorithms represent an appealing alternative for combining measurements in large-scale networks of low-capable sensors, due to their low computational cost and strong convergence properties. However, the actual performance of average consensus algorithms...
This paper compares the stochastic convergence of the Uniform Random number generators of two simulation software namely Matlab and Python and establishes the significance in choosing the right random number generator for error propagation studies. It further discusses about the application of Gaussian type of these random number generators to nonlinear cases of Error propagation using the Monte Carlo...
The α-stable distribution is highly intractable for inference because of the lack of a closed form density function in the general case. However, it is well-established that the α-stable distribution admits a Poisson series representation (PSR) in which the terms of the series are a function of the arrival times of a unit rate Poisson process. In our previous work, we have shown how to carry out inference...
This work examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gradient vector. The selection of which coordinates to use varies randomly from iteration to iteration and from agent to agent across the network....
Parallel and distributed processing is employed to accelerate training for many deep-learning applications with large models and inputs. As it reduces synchronization and communication overhead by tolerating stale gradient updates, asynchronous stochastic gradient descent (ASGD), derived from stochastic gradient descent (SGD), is widely used. Recent theoretical analyses show ASGD converges with linear...
In this paper, we study the parameters identification problem of Permanent Magnet Synchronous Motor (PMSM) in steady state. First, the controlled auto-regressive (CAR) model of PMSM is established. Secondly, based on the obtained CAR model, an improved stochastic gradient algorithm is proposed to identify the electrical parameters of PMSM. By introducing a tuning parameter in the presented algorithm,...
This work presents a review of the progress in the development of the numerical methods for sample paths simulation of stochastic differential equations. The error of approximation, the order of the convergence, the stability improvement for Euler methods (explicit, implicit and composite schemes) are studied theoretically as well as numerically. MathLab programs support all the illustrations.
In this paper, we propose a bounded-confidence opinion model and focus on the study of the opinion consensus probability based on long-range opinion interactions. In this model, each agent updates its opinion using some agents' opinion values according to its confidence bound. We provide a lower bound of the opinion consensus probability when the confidence bound is sufficiently small. Also, we give...
We investigate the problem of distributed source seeking with velocity actuated and force actuated vehicles by developing distributed Kiefer-Wolfowitz algorithm. First, based on stochastic approximation algorithm with expanding truncations, we present the distributed Kiefer-Wolfowitz algorithm, in which two noisy observations of each agent's objective function is used to estimate its gradient and...
Traditional convergence theory of self-tuning regulators requires boundedness of the conditional variances of the systems noise processes. However, this requirement cannot be satisfied for many practical models such the well-known ARCH (Autoregressive Conditional Heteroscedasticity) model in economic systems. The aim of this paper is to provide a convergence theory of self-tuning regulators for linear...
Because the class of wideband noise is quite realistic in applications and is a good approximation to white noise, this paper focuses on the asymptotic properties of integro-differential systems with wideband noise perturbations. Using perturbed test function methods combined with the martingale averaging techniques, and weak convergence methods, it is shown that when the small parameter tends to...
This paper defines the stochastic discrete higherorder sliding mode. A control input is designed for an uncertain stochastic system with partial state information such that stochastic discrete higher-order sliding mode takes place. The proposed definition and control scheme is validated through the simulation of a rectilinear plant.
Consider a data source comprised of a graph with marks on its edges and vertices. Examples of such data sources are social networks, biological data, web graphs, etc. Our goal is to design schemes that can efficiently compress and store such data. We aim for universal compression, i.e. without making assumptions about the stochastic properties of the data. To make sense of this, we employ the framework...
In this article, a novel multilevel Monte Carlo (MLMC) simulation approach is applied for large distribution systems reliability evaluation. Basic Monte Carlo simulation (MCS) can be effectively used in this purpose. However, main limitation of MCS is the huge computational cost when a large sample size is needed for a high accuracy. The MLMC method reduces the variance of MCS and speeds up its computational...
This paper proposes an alternative approach to analyze the convergence of multistage deterministic dual dynamic programming (DDDP). A new convergence stop criteria is proposed and it is based on a measure of gain produced when a new cut is inserted in cost-to-go function, during the backward phase of the algorithm. To do so, an additional linear programming problem is solved to decide if new cut (hyper...
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