Joint flexibility is an important factor to consider in the robot control design if high performance is expected for the robot *** works on control of rigid-link flexible-joint(RLFJ)robot in literature have assumed th...
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Joint flexibility is an important factor to consider in the robot control design if high performance is expected for the robot *** works on control of rigid-link flexible-joint(RLFJ)robot in literature have assumed that the kinematics of the robot is known *** have been few results that can deal with the kinematics uncertainty in RLFJ *** this paper,we propose an adaptive tracking control method which can deal with the kinematics uncertainty and uncertainties in both link and actuator dynamics of the RLFJ robot *** observers are designed to avoid accelerations measurement due to the fourth-order overall system *** stability of the closed-loop system is shown and sufficient conditions are presented to guarantee the stability.
The maximum clique problem has diverse applications in the field of pattern recognition, computer vision, information processing etc. The connection between self-assembly and computation has implied that the tile asse...
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Membrane systems, also called P systems, are biologically inspired theoretical models of distributed and parallel computing. Tissue P system with cell separation is a computing model in the frame work of membrane comp...
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Randí;et al. proposed a famous spectral graphical representation of DNA sequences, and claimed that it avoids loss of information. In this paper we build two mathematical models for this graphical representation ...
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For given graphs G1,G2, the 2-color Ramsey number R(G1,G2) is defined to be the least positive integer n such that every 2-coloring of the edges of complete graph Kn contains a copy of G1 colored with the first color ...
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For given graphs G1,G2, the 2-color Ramsey number R(G1,G2) is defined to be the least positive integer n such that every 2-coloring of the edges of complete graph Kn contains a copy of G1 colored with the first color or a copy of G2 colored with the second color. In this note, we obtained some new exact values of generalized Ramsey numbers such as cycle versus book, book versus book, complete bipartite graph versus complete bipartite graph.
The Ramsey multiplicity M(G) of a graph G is defined to be the smallest number of monochromatic copies of G in any two-coloring of edges of K R(G), where R(G) is the smallest integer n such that every graph on n verti...
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The Ramsey multiplicity M(G) of a graph G is defined to be the smallest number of monochromatic copies of G in any two-coloring of edges of K R(G), where R(G) is the smallest integer n such that every graph on n vertices either contains G or its complement contains G. With the help of computer algorithms, we obtain the exact values of Ramsey multiplicities for most of isolate-free graphs on five vertices, and establish upper bounds for a few others.
In this paper, we propose a novel unsupervised evolutionary clustering algorithm for mixed type data, evolutionary k-prototype algorithm (EKP). As a partitional clustering algorithm, k-prototype (KP) algorithm is a we...
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In this paper, we propose a novel unsupervised evolutionary clustering algorithm for mixed type data, evolutionary k-prototype algorithm (EKP). As a partitional clustering algorithm, k-prototype (KP) algorithm is a well-known one for mixed type data. However, it is sensitive to initialization and converges to local optimum easily. Global searching ability is one of the most important advantages of evolutionary algorithm (EA), so an EA framework is introduced to help KP overcome its flaws. In this study, KP is applied as a local search strategy, and runs under the control of the EA framework. Experiments on synthetic and real-life datasets show that EKP is more robust and generates much better results than KP for mixed type data.
In this paper, a novel constrained multiobjective immune algorithm for optimizing detector distribution in V-detector negative selection is proposed. The theory of artificial immune system (AIS) and the spirit of popu...
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In this paper, a novel constrained multiobjective immune algorithm for optimizing detector distribution in V-detector negative selection is proposed. The theory of artificial immune system (AIS) and the spirit of population evolution are introduced to generate detectors. By combining the constraint handling technique and AIS-based multiobjective optimization, the algorithm is able to steadily maximize the anomaly coverage with little extra cost, which means the distribution with maximized coverage of the non-self space and minimized overlapping among detectors with fixed size will be well realized. Furthermore, the new approach is tested on some benchmark problems. The experimental results show that compared with some state-of-the-art methods, our algorithm can remarkably outperform them in terms of enhancing the detection rate by optimizing distribution without increasing the number of detectors.
Based on clonal selection principle and the immunodominance theory, a new immune clustering algorithm, Immunodomaince based Clonal Selection Clustering Algorithm (ICSCA) is proposed in this paper. An immunodomaince op...
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Based on clonal selection principle and the immunodominance theory, a new immune clustering algorithm, Immunodomaince based Clonal Selection Clustering Algorithm (ICSCA) is proposed in this paper. An immunodomaince operator is introduced to the clonal selection algorithm, which can realize on-line gaining prior knowledge and sharing information among different antibodies. The proposed method has been extensively compared with Fuzzy C-means (FCM), Genetic Algorithm based FCM (GAFCM) and Clonal Selection Algorithm based FCM (CSAFCM) over a test suit of several real life datasets and synthetic datasets. The result of experiment indicates the superiority of the ICSCA over FCM, GAFCM and CSAFCM on stability and reliability for its ability to avoid trapping in local optimum.
Vector Quantization (VQ) is a useful tool for data compression and can be applied to compress the data vectors in the database. The quality of the recovered data vector depends on a good codebook. Mean/residual vector...
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Vector Quantization (VQ) is a useful tool for data compression and can be applied to compress the data vectors in the database. The quality of the recovered data vector depends on a good codebook. Mean/residual vector quantization (M/RVQ) has been shown to be efficient in the encoding time and it only needs a little storage. In this paper, Clonal Selection Algorithm for image Compression (CSAIC) is proposed. In CSAIC, Based on M/RVQ algorithm, an improved clonal selection algorithm is used to cluster the data of compressed images in order to obtain the optimal codebook. The proposed method has been extensively compared with Linde-Buzo-Gray(LBG), Self-Organizing Mapping (SOM) and Modified K-means(Mod-KM) over a test suit of seven natural images. The experimental results show that CSAIC outperforms other three algorithms in terms of image compression performance.
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