Aiming at the shortcomings of fuzzy C-means (FCM) clustering algorithm that it is easy to fall into local minima and sensitive to initial values and noisy data, this paper proposes a fuzzy clustering algorithm based o...
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ISBN:
(纸本)9783030787424;9783030787431
Aiming at the shortcomings of fuzzy C-means (FCM) clustering algorithm that it is easy to fall into local minima and sensitive to initial values and noisy data, this paper proposes a fuzzy clustering algorithm based on improved lion swarm optimization algorithm. Aiming at the problem that lion swarm optimization (LSO) algorithm is easy to fall into the local optimum, this paper improves lion swarm optimization algorithm by introducing sin cos algorithm and elite opposition-based learning. In addition, the introduction of a supervision mechanism enhances the lions' ability to jump out of local optimum and improves the local search ability of lion swarm optimization algorithm. The optimal solution obtained by improved lion swarm optimization algorithm is used as the initial clustering center of FCM algorithm, then FCM algorithm is run to obtain the global optimal solution, which effectively overcomes the shortcomings of FCM algorithm. The experimental results show that, compared with original FCM clustering algorithm, FCM clustering algorithm based on improved lion swarm optimization algorithm has improved the algorithm's optimization ability and has better clustering results.
In this paper, an adaptive intelligent controller is developed for the velocity-tracking problem of a nonholonomic wheeled mobile robot (WMR) in the presence of external disturbances and measurement noises. The whole ...
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In this paper, an adaptive intelligent controller is developed for the velocity-tracking problem of a nonholonomic wheeled mobile robot (WMR) in the presence of external disturbances and measurement noises. The whole control system is consisting of two subsystems, where each subsystem has its own control responsibility. In this way, first, a kinematic controller is implemented according to the kinematic model of the robot, and then a dynamic controller is designed based on the characteristic of the robot dynamics. Our focus is designing and developing an adaptive fractional-order fuzzy logic proportional-integral-derivative (FOFPID) controller for the trajectory-tracking task in a two-WMR. Unlike the prevalent works which only designed the scaling factors of FOFPID, a simultaneous optimization of fuzzy membership functions and controller coefficients are realized to improve the efficiency of the WMR dynamic controller. Accordingly, the controller parameters are optimally adjusted by employing a combination of the sin cos algorithm and harmony search, called SCA-HS. To validate the applicability of the suggested framework, experimental studies are also conducted on a real-time platform using a two-WMR prototype. The experimental results confirm the effectiveness of the proposed controller for the exact trajectory-tracking problem in the presence of disturbances and noises.
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