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As one widely applied swarm intelligent algorithm, particle swarm optimization (PSO) algorithm has obtained the attention of various scholars with its advantages of easy implementation, high precision and fast convergence. Firstly, aiming to solve the problems that PSO has low searching speed and PSO is easy to fall into local optimal solution especially when dealing with high-dimension model, this...
Interactive multiobjective optimization (IMO) methods aim at supporting human decision makers (DMs) to find their most preferred solutions in solving multiobjective optimization problems. Due to the subjectivity of human DMs, human fatigue, or other limiting factors, it is hard to design experiments involving human DMs to evaluate and compare IMO methods. In this paper, we propose a framework of a...
We consider the estimation of the state transition matrix in vector autoregressive models when the time sequence data is limited but nonsequence steady-state data is abundant. To leverage both sources of data, we formulate the problem as the least-squares minimization regularized by a Lyapunov penalty. Explicit cardinality or rank constraints are imposed to reduce the complexity of the model. The...
Complex kernel-based adaptive algorithms have been recently introduced for complex-valued nonlinear system identification. These algorithms are built upon the same framework as complex linear adaptive filtering techniques and Wirtinger's calculus in complex reproducing kernel Hilbert spaces. In this paper, we study the convergence behavior of the augmented complex Gaussian KLMS algorithm. Simulation...
Cooperative Co-evolutionary Genetic Algorithm (C-CGA) is an effective way to solve complex problems like high-dimensional and multi-Objective problem, but there are also performance issue of high time complexity in the application of the algorithm. For the issue of collaborator selection is a key element of the success of applying the algorithm, the paper proposes a new method to select collaborators...
In this paper, we study the consensus problem for second-order heterogeneous multi-agent systems under a directed graph. Unlike the existing consensus algorithms for second-order multi-agent systems in which all agents are assumed to have common unit inertias or share common control gains, we allow the inertias and the control gains to be heterogeneous for each agent. Fully distributed consensus algorithms...
Nonlinear adaptive filtering with kernels has become a topic of high interest over the last decade. A characteristics of kernel-based techniques is that they deal with kernel expansions whose number of terms is equal to the number of input data, making them unsuitable for online applications. Kernel-based adaptive filtering algorithms generally rely on a two-stage process at each iteration: a model...
Explicit feedback based congestion control schemes can capture network congestion status more accurately than pure end-to-end schemes. However, some of such schemes require modifying IP header in order to achieve near optimal performance, which incurs complicated computation in routers as well as makes them difficult to deploy in real networks. In contrast, the VCP protocol achieves good performance...
Wireless sensor networks are designed to perform on inference the environment that they are sensing. Due to the inherent physical characteristics of systems under investigation, non-negativity is a desired constraint that must be imposed on the system parameters in some real-life phenomena sensing tasks. In this paper, we propose a kernel-based machine learning strategy to deal with regression problems...
Orthogonal Frequency Division Multi-plexing (OFDM) is a significant technology to provide a high-rate wireless transmissions. In this paper, a scheme called as a step size delta - least mean square (SSD-LMS) is used for phase error cancellation. The results show that SSD-LMS algorithm can provide a rapid convergence with a low steady-state fluctuation error than that with the conventional LMS algorithm.
In this paper, a general game model, called cost constrained resource selection game (CC-rsg) is presented to model and analyze the setting where agents with cost constraint competitively share a global set of resources. We prove that a Nash equilibrium exists in any instance of CC-rsg. In addition, we study a no-regret learning process in which agents play with two observations of so-called recency...
This paper develops finite-time consensus theory for multi-agent systems and presents the design and analysis results of distributed consensus protocols, which are continuous state feedbacks. Those distributed algorithms are in general form and have their wide range of applications, including fast consensus, saturation control, and network connectivity preserving. By employing the tools of finite-time...
This paper presents a new frequency-domain adaptive algorithm for sparse echo cancellation by incorporating a sparse partial (SP) subblock selection scheme into the multi-delay filtering (MDF) algorithm. Unlike the sparse partial tap selection method, the proposed algorithm selects the active regions based on FFT subblocks such that frequency bins within each subblock can be updated evenly. Simulation...
Combined forecast method is an important research direction in forecast field. The relevant research has shown many advantages of combined forecast. However, how to acquire efficiently the weight coefficients of combined forecast method to get the predicted value with minimum error is often hard to solve efficiently. Because the adaptive parameter real-coded genetic algorithm (APRGA) not only has...
In this paper, we propose utility-based power control algorithm jointing with admission control (UPCAC) for distributed communication in the hierarchical spectrum sharing network (HSSN) for cognitive radio. We formulate a utility function (payoff function minus price function) to reflect the tradeoff policy between the protecting for primary users (PUs) and the supporting to quality of service (QoS)...
This paper discusses a direct-sequence ultra-wideband (DS-UWB) system that uses the least-mean-square (LMS) adaptive filter for interference cancellation. Compared to the conventional LMS algorithm, the fuzzy step size LMS algorithm which we propose can perform rapid convergence with a low steady-state fluctuation error. The sequential partial update scheme is used in the proposed fuzzy step size...
In this work, we formulate the potential game model of joint channel selection and power allocation. First, under the interference constraint, a nonlinear optimization problem is formulated for improving the total throughput and considering the fairness in cognitive radio network. we also define the special objective function for each transmitting node and formulate a potential game to solve this...
Marriage in Honey Bees Optimization (MBO) is a new optimization technique to simulate the social system. But the calculation process is complex and the speed is slow. The paper proposed a faster Marriage in Honey Bees Optimization (FMBO) algorithm with global convergence. By randomly initializing drones and restricting the condition of iteration, the computation process becomes easier and faster....
We consider a worse case control oriented identification problem recently studied by several authors. This problem is one of the H?? identification in the continuous time setting. We give a less conservative formulation of this problem. The available apriori information consists of a lower bound on the relative stability of the plant, a frequency dependent upper bound on a certain gain associated...
In this paper we formulate and solve a control-oriented system identification problem for single-input, single-output, linear, shift-invariant, distributed parameter plants. In this problem the available apriori information is minimal, consisting only of worst-case/deterministic, time dependent, upper and lower bounds on the plant impulse response and the additive output noise. The available aposteriori...
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