An implementation of adaptive filtering, composed of an unsupervised adaptive filter (UAF), a multi-step forward linear predictor (FLP), and an unsupervised multi-step adaptive predictor (UMAP), is built for sup...
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An implementation of adaptive filtering, composed of an unsupervised adaptive filter (UAF), a multi-step forward linear predictor (FLP), and an unsupervised multi-step adaptive predictor (UMAP), is built for suppressing impulsive noise in unknown circumstances. This filtering scheme, called unsupervised robust adaptive filter (URAF), possesses a switching structure, which ensures the robustness against impulsive noise. The FLP is used to detect the possible impulsive noise added to the signal, if the signal is "impulse-free", the filter UAF can estimate the clean sig- nal. If there exists impulsive noise, the impulse corrupted samples are replaced by predicted ones from the FLP, and then the UMAP estimates the clean signal. Both the simulation and experimental results show that the URAF has a better rate of convergence than the most recent universal filter, and is effective to restrict large disturbance like impulsive noise when the universal filter fails.
This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics differen...
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This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.
This paper addresses the problem of distributed connectivity constrained motion coordination of multiple autonomous mobile agents. Different from traditional flat network structure which lacks flexibility and scalabil...
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In this paper, an improved adaptive time-varying sliding mode controller (SMC) is designed for five-phase dual-rotor permanent magnet synchronous motor (FDRPMSM). It focuses on three objectives: 1) maintaining the hig...
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Complete coverage path planning is a key problem for autonomous mobile robot, which concerns both efficiency and completeness of coverage. This paper proposed a novel strategy of combined coverage path planning method...
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Considering the stochastic nature of electric vehicles (EVs) charging activities, this paper is dedicated to schedule the resident EVs charging load in the smart grid. Three important factors of the EV charging proces...
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This paper presents a new soft switching bidirectional buck or boost DC-DC converter. Compared to the traditional bidirectional DC-DC converter, the new topology can be used as a buck converter or a boost converter in...
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ISBN:
(纸本)9787506292214
This paper presents a new soft switching bidirectional buck or boost DC-DC converter. Compared to the traditional bidirectional DC-DC converter, the new topology can be used as a buck converter or a boost converter in bidirectional cases for Hybrid Electric Vehicles (HEV) and Electrosorb Technology (EST) etc. This new converter has the advantages of simple circuit and control strategy, soft-switching implementation without additional devices, high power density, low cost, light weight and high reliability. The operating principle, theoretical analysis, and design guidelines are provided in this paper. The simulation and the experimental verifications are also presented.
Launch equipment hydraulic system plays a vital role in completion of mission due to its stability and accuracy. This paper built an effectiveness evaluation index system based on comprehensive analysis of launch equi...
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ISBN:
(纸本)9781467374439
Launch equipment hydraulic system plays a vital role in completion of mission due to its stability and accuracy. This paper built an effectiveness evaluation index system based on comprehensive analysis of launch equipment hydraulic system, compared weighting factor determination mechanisms, and integrated independence factor into weight determination by combining the strong coupling feature of launch equipment hydraulic system indexes. The final weight was integrated and determined in four aspects, including importance weight, information weight, independence weight and credibility weight. Then, the paper obtained the system effectiveness evaluation results by utilizing system operation data, and provided theoretical and data reference for equipment selection and effectiveness improvement.
This paper presents extensive experiments on a hybrid optimization algorithm (DEPSO) we recently developed by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and p...
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This paper presents extensive experiments on a hybrid optimization algorithm (DEPSO) we recently developed by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). The hybrid optimizer achieves on-the-fly adaptation of evolution methods for individuals in a statistical learning way. Two primary parameters for the novel algorithm including its learning period and population size are empirically analyzed. The dynamics of the hybrid optimizer is revealed by tracking and analyzing the relative success ratio of PSO versus DE in the optimization of several typical problems. The comparison between the proposed DEPSO and its competitors involved in our previous research is enriched by using multiple rotated functions. Benchmark tests involving scalability test validate that the DEPSO is competent for the global optimization of numerical functions due to its high optimization quality and wide applicability.
Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control prot...
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