In this paper, we consider the online source localization problem by using the time difference of arrival (TDOA) of the base station and a moving unmanned aerial vehicle (UAV). First, we transform the source localization problem into estimating an unknown parameter of a stochastic linear regression model. Then, we propose a modified stochastic gradient (SG) algorithm to estimate the unknown parameter, and establish sufficient conditions on the UAV’s positions to guarantee global convergence of the algorithm. We note that our convergence results are obtained without using the persistent excitation condition or the independent and identical distributed assumptions on the data. Finally, numerical simulations are presented to validate the effectiveness of the proposed algorithm.

This paper considers the parameter learning and data clustering problem for MLR with multiple sub-models and arbitrary mixing weights. To deal with the data streaming case, we propose an online learning algorithm to estimate the unknown parameters. By utilizing Ljung’s ODE method, we establish the almost sure convergence results of this MLR problem without the traditional i.i.d. assumption on the input data for the first time. Based on the convergence property and using the classical stochastic Lyapunov function method, we also obtain the convergence rate analysis of the proposed algorithm for the first time. In addition, the data clustering can asymptotically achieve the same performance as the case with known parameters. Future work will consider how to relax the asymptotically stationary and ergodic assumption on the input data, and how to design algorithms with global convergence performance for the MLR problem.

In this article, a distributed stochastic gradient (SG) algorithm is proposed where the estimators are aimed to collectively estimate an unknown time-invariant parameter from a set of noisy measurements obtained by distributed sensors. The proposed distributed SG algorithm combines the consensus strategy of the estimation of neighbors with the diffusion of regression vectors. For the theoretical investigation of the proposed algorithm, the main challenge lies in analyzing the influence of the Laplacian matrix on the state transition matrix and the properties of the product of nonindependent and nonstationary random matrices. Some analysis techniques such as graph theory and martingale theory are used to deal with the above issues. A cooperative excitation condition is introduced, under which the convergence of the distributed SG algorithm can be obtained without relying on the independency or stationarity assumptions of regression vectors which are commonly used in the existing literature. Furthermore, the convergence rate of the algorithm can be established. Finally, we show that all the sensors can cooperate to fulfill the estimation task even though any individual sensor cannot by a simulation example.

Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry, and atomic physics. In this paper, an improved differential evolution algorithm, referred to as multiple-samples and mixed-strategy DE (msMS_DE), is proposed to search robust fields for various quantum control problems. In msMS_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMS_DE algorithm is applied to the control problems of: 1) open inhomogeneous quantum ensembles and 2) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMS_DE is experimentally implemented on femtosecond (fs) laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH2BrI. The experimental results demonstrate the excellent performance of msMS_DE in searching for effective fs laser pulses for various tasks.

This paper considers the consensus behavior of a spatially distributed 3-D dynamical network composed of heterogeneous agents: leaders and followers, in which the leaders have the preferred information about the destination, while the followers do not have. All followers move in a 3-D Euclidean space with a given speed and with their headings updated according to the average velocity of the corresponding neighbors. Compared with the 2-D model, a key point lies in how to analyze the dynamical behavior of a "linear" nonhomogeneous equation where the nonhomogeneous term strongly nonlinearly depends on the states of all agents. Using the network structure and the estimation of some characteristics for the initial states, we present a proper decaying rate for the nonhomogeneous term and then establish lower bounds on the ratio of the number of leaders to the number of followers that is needed for the expected consensus by considering two cases: 1) fixed speed and neighborhood radius and 2) variable speed and neighborhood radius with respect to the population size. Some simulation examples are given to justify the theoretical results.This paper considers the consensus behavior of a spatially distributed 3-D dynamical network composed of heterogeneous agents: leaders and followers, in which the leaders have the preferred information about the destination, while the followers do not have. All followers move in a 3-D Euclidean space with a given speed and with their headings updated according to the average velocity of the corresponding neighbors. Compared with the 2-D model, a key point lies in how to analyze the dynamical behavior of a "linear" nonhomogeneous equation where the nonhomogeneous term strongly nonlinearly depends on the states of all agents. Using the network structure and the estimation of some characteristics for the initial states, we present a proper decaying rate for the nonhomogeneous term and then establish lower bounds on the ratio of the number of leaders to the number of followers that is needed for the expected consensus by considering two cases: 1) fixed speed and neighborhood radius and 2) variable speed and neighborhood radius with respect to the population size. Some simulation examples are given to justify the theoretical results.

This paper focuses on the synchronization analysis of a class of multiagent systems, where both speed and heading of each agent depend on the states of its local neighbors. The neighbors are defined through the distance between agents and all agents are interconnected via proximity nets. In the variable speed model, the speed of each agent depends on the polarization order of its neighbors in a power-law manner, and the heading is updated according to the average heading of its neighbors. Therefore, the speeds, headings, and positions of all agents are strongly coupled together. For the uniformly and independently distributed initial states, we provide sufficient conditions, imposed only on model parameters, to guarantee synchronization of the variable speed model in the following two cases: 1) the maximum speed and the neighborhood radius are fixed constants and 2) the maximum speed and the neighborhood radius are changing with the population size. Our results reveal that the permitted maximum speed in the variable speed model can be larger than that in the relevant constant speed model.

As social network services (SNS) are gaining popularity, establishing trust relationships among users in SNS is necessary for user interaction and decision making. However, the evaluation of global trust values (i.e. reputation) of users suffers from the curse of opinion divergence in the network. In this paper, we consider the scenario of opinion divergence within social networks, and propose a method to reach different global trust values between groups of users with controversial opinions in the network. We model the global trust opinion formation in discrete-time dynamics and introduce bipartite consensus as the approach to establish global trust in such circumstances. Such approach works upon the property of structural balance, and can be extended to more general situations where eventual positivity applies. Via our approach, non-trivial global trust can be reached within the network, which can guide users' social behaviors, and support many SNS-based applications.