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Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles’ local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators.

This paper focuses on examining the key factors that affect poor households in the Central Highlands by using the Ordinary Least Square (OLS) methods to analyze the Vietnam Living Standard Survey (VLSS) in 2008 panel dataset. In the 597 surveyed households in the Central Highlands, we selected 95 poor households to make research group. The empirical findings indicate that income per capita of poor households has been affected by many factors in which the strongest impact is from education level, size of households and marital status. Furthermore, the empirical study shows that most poor households in this research are ethnic minorities with large scale, low level of education and occupational skills.

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