Applied Mechanics and Materials Vols. 333-335

Abstract: Designing the approximate reasoning matching schemes and the corresponding algorithms with similarity relation Q instead of equivalence relation R can reflect the nature of approximate reasoning and meet more application expectations. In this paper, we introduce the type V matching scheme and the corresponding type V Q-algorithm of multiple multidimensional approximate reasoning with given multidimensional input and multidimensional knowledge bases on strong Q-logic CQ.

Abstract: Vehicle routing problem (VRP) is a kind of NP combination problem. In order to overcome PSOs premature convergence, a Cloud Particle Swarm Optimization (CPSO) is put forward, it uses the randomicity and stable tendentiousness characteristics of cloud model, adopts different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation is balanced effectively. In the paper, the algorithm is used to solve VRP, a kind of new particles coding method is established and the solution algorithm is developed. The simulation results prove that the algorithm has more search speed and stronger optimization ability than GA and the PSO algorithm.

Abstract: The aim of this work is to consider an iterative method for a-strict pseudo-contractions. Strong convergence theorems are established in a real 2-uniformly smooth Banach space.

Abstract: Different from the existing multiple kernel methods which mainly work in implicit kernel space, we propose a novel multiple kernel method in empirical kernel mapping space. In empirical kernel mapping space, the combination of kernels can be treated as the weighted fusion of empirical kernel mapping samples. Based this fact, we developed a multiple kernel Fisher method to realize multiple kernel classification in empirical kernel mapping space. The experiments here illustrate that the proposed multiple kernel fisher method is feasible and effective.

Abstract: Clustering analysis is grouping a set of physical or abstract objects into the similar class. In traditional clustering algorithm, objects are usually divided into a certain cluster. This paper applies the risk evaluation of decision-theoretic rough set model in clustering analysis which solves the problem of uncertain boundary region, and proposes a hierarchical clustering algorithm of the minimum risk which can adjust threshold value to construct a clustering evaluation function in order to find the solution to optimize the result. At last, the case analysis shows the algorithm is feasible. It could provide a strong support for marine environment monitoring system and so on.

Abstract: This paper presents an improved metaheuristic algorithm to minimize the makespan in a hybrid flow-shop scheduling (HFS) with non-identical parallel machines. First, a mathematical model for an HFS problem is introduced. Second, an improved simulated annealing algorithm (ISAA) which is inspired from a hormone modulation mechanism is presented to retrofit speed and accuracy of the algorithm. Finally, the computer simulation demonstrates the good quality of the proposed procedure, and it outperforms several other algorithms.

Abstract: FCM(Fuzzy C-Means) algorithm is an important algorithm in cluster analysis. It plays an significant role in theory and practice. However, the clustering number of FCM algorithm needs to be set beforehand. This paper proposes an automatic clustering number determination for the classical FCM(Fuzzy C-Means) algorithm. The proposed automatic clustering number determination is based on the cardinality of clustering fuzzy membership used in the CA(Competitive Agglomeration) algorithm. The effectiveness of the proposed algorithm, along with a comparison with CA algorithm, has been showed both qualitatively and quantitatively on a set of real-life datasets.

Abstract: Air traffic is increasing worldwide at a steady annual rate, and airport congestion is already a major issue for air traffic controllers. The traditional method of traffic flow prediction is difficult to adapt to complex air traffic conditions. Due to its self-learning, self-organizing, self-adaptive and anti-jamming capability, the hybrid fuzzy neural network can predict more effectively the air traffic flow than the traditional methods can. A good method for training is an important problem in the prediction of air traffic flow with neural network. This paper will try to find a new model to solve the traffic flow prediction problem by hybrid fuzzy neural network.

Abstract: Swarm automated planning algorithm is a planning method based on planning graph technology, and the algorithm which could improve the searching efficiency through importing swarm intelligence has the character of global and parallel. Yet sometimes the algorithm has the shortcoming in the local searching. For instance, maybe the quality of the best candidate planning solution would get lower after another around of searching. To enhance the ability of the local searching and the convergence acceleration, the swarm automated planning algorithm which with the local repairing operators is presented in this paper. The searching efficiency could be bettered through the pertinence repairing operators and the heuristic evaluation information to control the repairing processes.

Abstract: A feature selection algorithm based on the optimal hyperplane of SVM is raised. Using the algorithm, the contribution to the classification of each feature in the candidate feature set is test, and then the feature subset with best classification ability will be selected. The algorithm is used in the recognition process of storm monomers in weather forecast, and experimental data show that the classification ability of the features can be effectively evaluated; the optimal feature subset is selected to enhance the working performance of the classifier.