In this paper, we generalize the inclusion constrained longest common subsequence (CLCS) problem to the hybrid CLCS problem which is the combination of the sequence inclusion CLCS and the string inclusion CLCS, called...
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In this paper, we generalize the inclusion constrained longest common subsequence (CLCS) problem to the hybrid CLCS problem which is the combination of the sequence inclusion CLCS and the string inclusion CLCS, called the sequential substring constrained longest common subsequence (SSCLCS) problem. In the SSCLCS problem, we are given two strings A and B of lengths m and n, respectively, formed by alphabet Sigma and a constraint sequence C formed by ordered strings (C-1, C-2, C-3, ... , C-l) with total length r. The problem is that of finding the longest common subsequence D of A and B containing C-1, C-2, C-3, ... , C-l as substrings and with the order of the C's retained. This problem has two variants, depending on whether the strings in C cannot overlap or may overlap. We propose algorithms with 0(mnl + (m + n)(vertical bar Sigma vertical bar + r)) and 0(mnr + (m + n)vertical bar Sigma vertical bar) time for the two variants. For the special case with one or two constraints, our algorithm runs in 0(mn + (m + n)(vertical bar Sigma vertical bar + r)) or 0(mnr + (m + n)vertical bar Sigma vertical bar) time, respectively-an order faster than the algorithm proposed by Chen and Chao. (c) 2012 Elsevier Inc. All rights reserved.
To enhance the approximation ability of process neural networks, a novel training algorithm is proposed by employing an improved quantum genetic algorithm. The proposed approach is applied to the training of process n...
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ISBN:
(纸本)9783037858646
To enhance the approximation ability of process neural networks, a novel training algorithm is proposed by employing an improved quantum genetic algorithm. The proposed approach is applied to the training of process neural networks. The number of genes in a single chromosome is equal to the number of weight parameters. Taking each qubit in the current optimal chromosome as the goal, all individuals are updated by quantum rotation gate. In this method, each chromosome has three chains of genes, which can accelerate convergence. Taking the pattern classification of trigonometric functions as an example, the experimental results show that the proposed method is obviously superior to the common process neural networks.
This paper considers secure communication in a multiuser multiple-input single-output (MISO) downlink system with simultaneous wireless information and power transfer. We study the design of resource allocation algori...
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ISBN:
(纸本)9781479928514
This paper considers secure communication in a multiuser multiple-input single-output (MISO) downlink system with simultaneous wireless information and power transfer. We study the design of resource allocation algorithms minimizing the total transmit power for the case when the receivers are able to harvest energy from the radio frequency. In particular, the algorithm design is formulated as a non-convex optimization problem which takes into account artificial noise generation to combat potential eavesdroppers, a minimum required signal-to-interference-plus-noise ratio (SINR) at the desired receiver, maximum tolerable SINRs at the potential eavesdroppers, and a minimum required power delivered to the receivers. We adopt a semidefinite programming (SDP) relaxation approach to obtain an upper bound solution for the considered problem. The tightness of the upper bound is revealed by examining a sufficient condition for the global optimal solution. Inspired by the sufficient condition, we propose two suboptimal resource allocation schemes enhancing secure communication and facilitating efficient energy harvesting. Simulation results demonstrate a close-to-optimal performance achieved by the proposed suboptimal schemes and significant transmit power savings by optimization of the artificial noise generation.
To improve the efficiency of particle swarm optimization, a quantum particle swarm optimization algorithm is proposed on the basis of analyzing the search process of particle swarm optimization algorithm. In the propo...
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ISBN:
(纸本)9781467347143
To improve the efficiency of particle swarm optimization, a quantum particle swarm optimization algorithm is proposed on the basis of analyzing the search process of particle swarm optimization algorithm. In the proposed algorithm, particles are endoded by qubits described on the Bloch sphere, each particle occupy three locations of the search space, and each location represents a optimization solution. By employing the search method of general PSO to adjust the two parameters of qubit, the qubits rotation are performed on the Bloch sphere, which can simultaneously update three locations occupied by a qubit and quickly approach the global optimal solution. The experimental results of standard test function extreme optimization and fuzzy controller parameters optimization show that the proposed algorithm is superior to other similar algorithm in optimization ability and optimization efficiency.
To solve prediction of sunspot number, a parallel process neural networks model is proposed in this paper, Firstly, by dividing the whole time-varying process into several small time intervals, the process neural netw...
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ISBN:
(纸本)9783037858646
To solve prediction of sunspot number, a parallel process neural networks model is proposed in this paper, Firstly, by dividing the whole time-varying process into several small time intervals, the process neural networks are constructed in these small time intervals, which may disperse the load of networks. Then, employing the orthogonal basis expansion in functional space, the learning algorithm of the above-mentioned model is designed. The experimental results of time series predication of sunspots show that the proposed method has great potential for complicated nonlinear time series prediction.
Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a...
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ISBN:
(纸本)9780769551098
Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a novel learning paradigm named label distribution learning (LDL) for such kind of applications. The label distribution covers a certain number of labels, representing the degree to which each label describes the instance. LDL is a more general learning framework which includes both single-label and multi-label learning as its special cases. This paper proposes six LDL algorithms in three ways: problem transformation, algorithm adaptation, and specialized algorithm design. In order to compare their performance, six evaluation measures are suggested for LDL algorithms, and the first batch of real-world label distribution datasets are prepared. Experimental results on the ten real-world datasets show clear advantage of the specialized algorithms, which indicates the importance of special design for the characteristics of the LDL problem.
BP artificial neural network(ANN) based on gradient algorithm method is widely applied, but because the error surface of object function is very complex and the choose of initial value effects network training results...
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ISBN:
(纸本)9783037857755
BP artificial neural network(ANN) based on gradient algorithm method is widely applied, but because the error surface of object function is very complex and the choose of initial value effects network training results, convergence rate is slow and local minimum is likely to fall into. Particle swarm optimization(PSO) algorithm has better global searching ability to get rid the puzzles of falling into local minimum. By adequately studying on the two algorithms' characteristics, a new type of combined ANN training method is put forward, and PSO-BP ann model is successfully built.
Studies show that both the personal preference and social tightness between friends play important roles in the decision process of activity participation for a person. Considering the preference of a person and the s...
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ISBN:
(纸本)9781479925285
Studies show that both the personal preference and social tightness between friends play important roles in the decision process of activity participation for a person. Considering the preference of a person and the social tightness among friends, in this work, we formulate a new research problem, called Package Organization for Willingness sAtisfaction (POWA), to effectively select items into a package that can be adopted by the most users. Efficiently obtaining the optimal package and the corresponding group of users under the setting of POWA is very challenging, as we prove that POWA is NP-hard. Aiming to strike a balance between the quality of solutions and the time needed for computation, we propose algorithm Intermediate Package Organization with Social and Preference Pruning (IPOSPP) to obtain good solutions efficiently. We conduct an extensive performance evaluation on four real datasets to demonstrate the performance of the proposed algorithm.
To improve the efficiency of particle swarm optimization, a random particle swarm optimization algorithm is proposed on the basis of analyzing the search process of quantum particle swarm optimization algorithm. The p...
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ISBN:
(纸本)9783037855898
To improve the efficiency of particle swarm optimization, a random particle swarm optimization algorithm is proposed on the basis of analyzing the search process of quantum particle swarm optimization algorithm. The proposed algorithm has only a parameter, and its search step length is controlled by a random variable value. In this model, the target position can be accurately tracked by the reasonable design of the control parameter. The experimental results of standard test function extreme optimization and clustering optimization show that the proposed algorithm is superior to the quantum particle swarm optimization and the common particle swarm optimization algorithm in optimization ability and optimization efficiency.
The existing transportation service system in public travel route can not satisfy the people's actual travel need because of various technologies reasons. In our study, we set the tourist attractions as a vertex, ...
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ISBN:
(纸本)9783037853801
The existing transportation service system in public travel route can not satisfy the people's actual travel need because of various technologies reasons. In our study, we set the tourist attractions as a vertex, and simplified the traditional algorithm for complex network computing. Aim to improve the disadvantage of tradition Dijkstra algorithm, an improve algorithm was proposed to improve the path search efficiency. Then the improved Dijkstra algorithm was applied to tourism path search. The experimental results have illustrated that the improved Dijkstra algorithm can accomplish a better result and improve path search efficiency.
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