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In Psychology, goal-setting theory, which has been studied by psychologists for over 35 years, reveals that goals play significant roles in incentive, action and performance for human beings. Based on this theory, the model of goal net has been proposed as a goal oriented agent model. The previous investigation has shown that the goal net model can support well multiple action and goal selection....
Cognitive maps (CMs), fuzzy cognitive maps (FCMs), and dynamical cognitive networks (DCNs) are related tools for modeling the cognition of human beings and facilitating machine inferences accordingly. FCMs extend CMs, and DCNs extend FCMs. Domain experts often face the challenge that CMs/FCMs are not sufficiently capable in many applications and that DCNs are too complex. This paper presents a simplified...
In Grid computing resource selection is a challenging problem, because Grid scheduler is usually operating in a dynamic and uncertain environment. Conventional scheduling algorithms will fail due to the static rules specified at design time and much user intervention required. Neural networks with a fast and accurate learning paradigm are promising to solve the Grid resource selection problem. This...
Recently Extreme Learning Machine (ELM) has been attracting attentions for its simple and fast training algorithm, which randomly selects input weights. Given sufficient hidden neurons, ELM has a comparable performance for a wide range of regression and classification problems. However, in this paper we argue that random input weight selection may lead to an ill-conditioned problem, for which solutions...
Resource selection in Grid involves great dynamics and uncertainties inherited from tasks and resources. The optimal selection of a resource against a task requires fast and intelligent services. Intelligent agent with fast learning capability is promising to resource selection problem in Grid. This paper proposes an Extreme Learning Machine (ELM)-based agent, in which an ELM connectionist module...
This paper first reviews extreme learning machine (ELM) in light of coverpsilas theorem and interpolation for a comparative study with radial-basis function (RBF) networks. To improve generalization performance, a novel method of combining a set of single ELM networks using stacked generalization is proposed. Comparisons and experiment results show that the proposed stacking ELM outperforms a single...
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