This paper presents the communication strategy for second-order multi-agent systems with nonlinear dynamics. To address the problem of the scarcity of communication channel resources and get rid of using continuous signals among the followers in lead-follower multi-agent systems, a novel event-triggered communication mechanism is proposed in this paper. To avoid employing the centralized information that depends on the Laplacian matrix spectrum, a network protocol with updated coupling gains is proposed, as well as an event-triggered strategy with updated thresholds. To eliminate the ill effects of inter-node communicating noise, relative positions are employed by the protocol instead of absolute positions. By a Lyapunov–Krasovskii functional, it is rigorously proven that the leader-following consensus of MASs is achieved without Zeno behavior, under the control of the proposed protocol with an event-triggered mechanism communication. The effectiveness of the proposed protocol is verified through numerical examples.
This paper presents a new BP-neural-networkbased localization algorithm for a wheeled agricultural mobile robot, which is front-wheel drive and differential steering. Training the BP neural network is the first step of the localization algorithm. During this process, the drive pulse number is regarded as the input; the length of the left or right wheel's trajectory is regarded as the output; and Bayesian rule is used to generate the training function. Inducing the displacement of the robot's geometric center is the second step, in which the trajectories of the left and right wheels are assumed as two concentric circular arces. In the contrast experiments with the least square localization algorithm, the new proposed BP-neural-network based algorithm shows high accuracy and feasibility.
Hidden Markov Random Field (HMRF) model and FiniteMixture Model (FMM) parameter estimation algorithm provides an interesting framework for image segmentation task, hence a technique that capitalizes on the benefits of both algorithms would achieve better performance. In this regard, we propose a new segmentation algorithm which combines with HMRF model and FMM parameter estimation algorithm. Firstly, we use a real-coded genetic algorithm based FMM to estimate image parameters. Secondly, according to the estimated image parameters, image pixels are classified into different classes through the HMRF segmentation framework. The performance of the proposed algorithm is tested on Berkeley image segmentation dataset. Experimental results have ennflrmed that the proposed algorithm offers a useful improvement of the segmentation accuracy over competing methodologies.
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