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In this work we analyze the application of Support Vector Machines for Regression (SVRs) to the problem of identifying weakly nonlinear systems. Examples of simple linear and nonlinear systems are considered, taking into account both non-recursive and recursive models. When defining the SVR estimating function, several kinds of kernels are employed, and the effect on the accuracy performance of reducing...
In this paper, we face the problem of designing accurate decision-making modules in measurement systems that need to be implemented on resource-constrained platforms. We propose a methodology based on multiobjective optimization and genetic algorithms (GAs) for the analysis of support vector machine (SVM) solutions in the classification error-complexity space. Specific criteria for the choice of optimal...
One prominent disadvantage of SVM when implemented in wireless sensor networks (WSNs) is the run-time complexity of classifier, which linearly increases with the number of support vectors (SVs). This disadvantage prevents applying SVM in some applications. In this paper, we propose an improved reduced set method to find solutions characterized by few number of vectors and having good generalization...
With the advancement of wireless and electronic technologies, wireless networks consist of tiny sensor devices hold the promise of revolutionizing sensing in a wide range of application domains because of their flexibility, low costs and ease of deployment. In this paper, the employment of ad-hoc wireless sensor networks to perform signals classification is proposed. For such application, the use...
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