Support vector machine, a universal method for learning from data, gains its development based on statistical learning theory. It shows many advantages in solving nonlinearly small sample and high dimensional problems...
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Support vector machine, a universal method for learning from data, gains its development based on statistical learning theory. It shows many advantages in solving nonlinearly small sample and high dimensional problems of pattern recognition. Only a part of samples or support vectors (SVs) plays an important role in the final decision function. But SVs could not be obtained in advance until a quadratic programming is performed. In this paper, we use K-nearest neighbour method to extract a boundary vector set which may contain SVs. The number of the boundary set is smaller than the whole training set. Consequently it reduces the training samples, speeds up the training of support vector machine.
Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin clonal selection algorithm (BCSA), is proposed to solve the optimal appro...
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Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin clonal selection algorithm (BCSA), is proposed to solve the optimal approximation of linear systems. For engineering computing, the novel algorithm adopts three operations to evolve and improve the population: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new algorithm have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm, multi-agent genetic algorithm and artificial immune response algorithm.
In this paper, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the infor...
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In this paper, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compared LCSA with the clonal selection algorithm (CSA) in solving twenty benchmark problems to test the performance of LCSA. The results demonstrate that LCSA is effective and efficient in solving numerical optimization problems.
Analytical study or designing of large‐scale nonlinear neural circuits, especially for chaotic neural circuits, is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framewo...
Analytical study or designing of large‐scale nonlinear neural circuits, especially for chaotic neural circuits, is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells’ dynamical equations. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system, and we proved that if a neural system works in a non‐chaotic way, a suitable fuzzy logical framework can be found and we can analyze or design such kind neural system similar to analyze or design a digit computer, but if a neural system works in a chaotic way, an approximation is needed for understanding the function of such neural system.
This paper aims at the combination of the artificial immune network and the support vector domain description for clustering. A new artificial immune antibody network is proposed. In the network, the antibody neighbor...
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This paper aims at the combination of the artificial immune network and the support vector domain description for clustering. A new artificial immune antibody network is proposed. In the network, the antibody neighborhood is represented as a support vector hypersphere, and an adaptive learning coefficient is presented. The input data set is firstly divided into subsets by antibodies, then each subset is mapped into a hypersphere respectively in a high dimensional feature space by support vector domain description. Finally the clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree, which need not a predefined number of clustering. The experimental results with several data sets illustrate the effectiveness of the proposed algorithm.
The essential characteristic of DNA computation is its massive parallelism in obtaining and managing information. With the develop- ment of molecular biology technique, the eld of DNA computation has made a great prog...
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The essential characteristic of DNA computation is its massive parallelism in obtaining and managing information. With the develop- ment of molecular biology technique, the eld of DNA computation has made a great progress. By using an advanced biochip technique, laboratory-on-a-chip, a new DNA computing model is presented in the paper to solve a simple timetabling problem, which is a special ver- sion of the optimization problems. It also plays an important role in education and other industries. With a simulated biological experiment, the result suggested that DNA computation with lab-on-a-chip has the potential to solve a real complex timetabling problem.
Multiobjective evolutionary clustering approach has been successfully utilized in data clustering. In this paper, we propose a novel unsupervised machine learning algorithm namely multiobjective evolutionary clusterin...
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Multiobjective evolutionary clustering approach has been successfully utilized in data clustering. In this paper, we propose a novel unsupervised machine learning algorithm namely multiobjective evolutionary clustering ensemble algorithm (MECEA) to perform the texture image segmentation. MECEA comprises two main phases. In the first phase, MECEA uses a multiobjective evolutionary clustering algorithm to optimize two complementary clustering objectives: one based on compactness in the same cluster, and the other based on connectedness of different clusters. The output of the first phase is a set of Pareto solutions, which correspond to different tradeoffs between two clustering objectives, and different numbers of clusters. In the second phase, we make use of the meta-clustering algorithm (MCLA) to combine all the Pareto solutions to get the final segmentation. The segmentation results are evaluated by comparing with three known algorithms: K-means, fuzzy K-means (FCM), and evolutionary clustering algorithm (ECA). It is shown that MECEA is an adaptive clustering algorithm, which outperforms the three algorithms in the experiments we carried out.
In this paper, we present a symmetrical invariant LBP (SILBP) texture descriptor based on the texture pattern equivalent, which is defined according to visual perception of textures. Then we extend the SILBP to linear...
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In this paper, we present a symmetrical invariant LBP (SILBP) texture descriptor based on the texture pattern equivalent, which is defined according to visual perception of textures. Then we extend the SILBP to linear SILBP. Our experiments on image retrieval show that the proposed texture discriptor has the advantages of symmetrical invariant, rotation robustness and computing simplicity.
According to the requirement of rapid variant design for products, it is vital that the complicated problem is transformed into the optimization problem. On the basis of it, an effective product optimization model of ...
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According to the requirement of rapid variant design for products, it is vital that the complicated problem is transformed into the optimization problem. On the basis of it, an effective product optimization model of variant design is established. An improved artificial immune algorithm is adopted, which simulates the protein polypeptide structure of the antibody, the clone selection principle and the concentration regulation of the immune system, and uses a new analytic approach for the similarity between the antibodies. Finally, an example is given to evaluate the performance of the proposed approach. The simulation results illustrate the effectiveness of the proposed method in variant design of products.
Bee Swarm Genetic Algorithm (BSGA), a new efficient algorithm is developed for designing DNA sequences that satisfy some combinatorial and thermodynamic constraints. In BSGA, the optimum individual of population selec...
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ISBN:
(纸本)9781424431977;9780769531618
Bee Swarm Genetic Algorithm (BSGA), a new efficient algorithm is developed for designing DNA sequences that satisfy some combinatorial and thermodynamic constraints. In BSGA, the optimum individual of population selected as a queen bee and a random population is introduced to reinforce the exploitation of Genetic Algorithm (GA) and increase the diversity of population. Based on the algorithm, a computer simulation for DNA encoding is conducted and the sequences are better than the previous known systems that can prove the efficiency and convergence of our algorithm.
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