نتایج جستجو برای: akers graphical algorithm
تعداد نتایج: 790770 فیلتر نتایج به سال:
We suggest a graphical system for analyzing and processing algorithm structure with a user-friendly interface and a set of necessary tools. The system is designed to make the process of visualization, modification, and analysis of algorithm graphs easier. The system provides algorithm flow graph building and analysis, the algorithm flow graph height and width adjustment, algorithm simplificatio...
This paper focuses on formants as basic parameters for vowels recognition. There are used two different algorithms for formants finding based on the LP algorithm: spectral peak picking and root extraction algorithm obtaining very good path estimations by each algorithm. Those methods are compared in a graphical form in our application ’WaveBlaster’.
A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG). There are basically two methods used for learning Bayesian network: parameter-learning and structure-learning. One of the most effective structure-learning methods is K2 algorithm. Because the performance of the K2 algorithm depends on node...
Power system state estimation is a process to find the bus voltage magnitudes and phase angles at every bus based on a given measurement set. The state estimation convergency is related to the sufficiency of the measurement set. Observability analysis actually tests this kind of problem and guarantees the state estimation accuracy. A new and useful algorithm is proposed and applied in this pape...
In this paper, a graphical algorithm (GrA) is presented for an investment optimization problem. This algorithm is based on the same Bellman equations as the best known dynamic programming algorithm (DPA) for the problem but the GrA has several advantages in comparison with the DPA. Based on this GrA, a fully-polynomial time approximation scheme is proposed having the best known running time. Th...
When the number of variables p is larger than the sample size n of a dataset generated from a Gaussian Graphical Model, the maximum likelihood estimation of the precision matrix does not exist. To circumvent this difficulty, in [14], the authors assume a faithful property on the models and propose a procedure based on conditioning on only one variable. The aim of this paper is to devise a new P...
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