Research on Learning Bayesian Networks by Particle Swarm Optimization
نویسندگان
چکیده
منابع مشابه
Particle Swarm Optimisation for learning Bayesian Networks
Specifically, we detail two methods which adopt the search and score approach to BN learning. The two algorithms are similar in that they both use PSO as the search algorithm, and the K2 metric to score the resulting network. The difference lies in the way networks are constructed. The CONstruct And Repair (CONAR) algorithm generates structures, validates, and repairs if required, and the REstr...
متن کاملBNC-PSO: structure learning of Bayesian networks by Particle Swarm Optimization
Structure learning is a very important problem in the field of Bayesian networks (BNs). It is also an active research area for more than two decades; therefore, many approaches have been proposed in order to find an optimal structure based on training samples. In this paper, a Particle Swarm Optimization (PSO)-based algorithm is proposed to solve the BN structure learning problem; named BNC-PSO...
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Using a Bayesian network (BN) learned from data can aid in diagnosing and predicting failures within a system while achieving other capabilities such as the monitoring of a system. However, learning a BN requires computationally intensive processes. This makes BN learning a candidate for acceleration using reconfigurable hardware such as fieldprogrammable gate arrays (FPGAs). We present a FPGA-...
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In this paper, a feedback learning particle swarm optimization algorithm with quadratic inertia weight (FLPSOQIW) is developed to solve optimization problems. The proposed FLPSO-QIW consists of four steps. Firstly, the inertia weight is calculated by a designed quadratic function instead of conventional linearly decreasing function. Secondly, acceleration coefficients are determined not only by...
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This paper presents particle swarm optimization based on learning from winner particle. (PSO-WS). Instead of considering gbest and pbest particle for position update, each particle considers its distance from immediate winner to update its position. Only winner particle follow general velocity and position update equation. If this strategy performs well for the particle, then that particle upda...
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ژورنال
عنوان ژورنال: Information Technology Journal
سال: 2006
ISSN: 1812-5638
DOI: 10.3923/itj.2006.540.545