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With the advent of sequencing technology, numerous gene expression data are generated. Identifying differentially expressed genes play an important role in the gene therapy of cancer patients. As an useful mathematical tool, nonnegative matrix factorization (NMF) has been successfully used for identifying differentially expressed genes. In this paper, a novel method named robust graph regularized...
Iterative learning control is applicable to systems that make sweeps or passes through dynamics defined over a finite duration. Once each pass is complete all information generated as its dynamics evolve are available for use in designing the control action to be applied on the next sweep. The design problem is to construct a sequence of control inputs to enforce convergence to a specified reference...
In [2], a distributed algorithm has recently been developed for solving linear algebraic equations via multi-agent networks. To adopt the algorithm, each agent only has to know part of the linear equation as well as its nearby neighbors' estimates to the solution. In this paper, we would like to further discuss this algorithm from the following two perspectives. The first one is to improve the numerical...
Iterative learning control(ILC) has been developed for processes or systems that complete the same finite duration task over and over again. The exact mode of operation is that after each execution is complete the system resets to the starting location ready for the start of the next one. Each execution is known as a trial and the duration the trial length. Once each trial is complete the information...
Iterative learning control has been developed for processes or systems that complete the same finite duration task over and over again. After each execution is complete, the system resets to the initial location, or a stoppage time occurs, and then the next execution can begin. In the literature each execution is commonly known as a trial and the duration is termed the trial length. Once a trial is...
There exist problems of slow convergence and local optimum in standard Q-learning algorithm. Truncated TD estimate returns efficiency and simulated annealing algorithm increase the chance of exploration. To accelerate the algorithm convergence speed and to avoid results in local optimum, this paper combines Q-learning algorithm, truncated TD estimation and simulated annealing algorithm. We apply improved...
Particle swarm optimizer (PSO) is a recently proposed population-based evolutionary algorithm, which exhibits good performance in many fields, and now itpsilas becoming more and more popular due to its strong global optimization capability and simple implementation. To achieve better performance, some variants investigated the utilization of different topologies in PSO. However, particles are only...
In the field of evolutionary algorithm, Differential Evolution (DE) has gained a great focus due to its strong global optimization capability and simple implementation. In DE, mutant vector, which plays the role of leading individuals to explore the search space, is generated by combining a base vector and a difference vector. However, these two vectors are selected either randomly or greedily according...
In this paper, we propose a new adaptive Differential Evolution algorithm, in which a simple mechanism based on Iterated Function System is applied to the control parameters F and CR. The performance is reported on a set of benchmark functions, which shows that our algorithm is better than, or at least comparable to the standard DE algorithm and the other adaptive versions of DE algorithm.
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