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Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. To enhance the selection of channel with less noise among the white spaces (idle channels), the a priory knowledge of Radio Frequency (RF) power is very important. Computational Intelligence (CI) techniques cans be applied to these scenarios...
On the basis of analyzing disadvantages of conventional prediction model of air-and-screen cleaning device, a new regression model based on support vector machine was proposed to predict and control of cleaning process precisely. Parameters of ε-SVR models were determined utilizing non-heuristic Grid Search, heuristic GA and PSO which could avoid the choice of randomness. The effect of samples in...
Software project development is a risky process with high failure rate. This paper proposes an intelligent model that can predict and control software development risks from an overall project perspective rather than focusing only on the single factor, project output. In this study, we first constructed a formal model for risk identification, and then collected actual cases from software development...
Innovation system efficiency analysis and prediction play an important role in regional innovation systems development and improve benefit of innovative capacity for country. According to the county innovation system data which is large scale and imbalance, this paper presented a support vector machine model to predict county innovation system efficiency. The method was compared with artificial neural...
Mine work face gas emission quantity is an important mine design basis, which also has important practical significance for guide mine design, ventilation and safety production. Mine gas emission quantity and work face multi factors have complex non-linear relationship. The paper built the work face gas emission prediction support vector machine (SVM) model. Based on data statistic of a mine work...
The research on the fouling prediction of heat exchanger is significantly to improve operational efficiency and economic benefits of the plants. Heat exchanger fouling prediction was introduced based on Support Vector Machine (SVM), and the Particle Swarm Optimization (PSO) was applied for optimizing the parameters of the support vector machine. One of the experiment databases of Heat exchanger fouling...
The problem of nonlinear time series prediction using integrated intelligent methods based on support vector machine (SVM) and particle swarm optimization (PSO) is studied. Aiming to the open problems of nonlinear time series prediction such as the best number of historical points and parameters of SVM are hard to be determined, a novel model for time series prediction based on PSO and SVM models...
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