01752nas a2200241   4500008004100000022001400041245008900055210006900144260009500213300001400308490000700322520087700329653002501206653001101231653002001242653001701262653001901279100001801298700002101316700002601337700002101363856012601384       2005                        eng d  a0263-876200aRegression models using pattern search assisted least square support vector machines0 aRegression models using pattern search assisted least square sup  a165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLANDbINST CHEMICAL ENGINEERScAUG  a1030-10370 v833 a<p>Least Square Support Vector Machines (LS-SVM), a new machine-learning tool has been employed for developing data driven models of non-linear processes. The method is firmly rooted in the statistical learning theory and transforms the input data to a higher dimensional feature space where the use of appropriate kernel functions avoid computational difficulty. Further, a pattern search algorithm, which explores multiple directions and utilizes coordinate search with fixed step size, is employed for selecting optimal LS-SVM model that produces a minimum possible prediction error. To show the efficacy and efficiency of the fully automated pattern search assisted LS-SVM methodology, we have tested it on several benchmark examples. The study suggests that proposed paradigm can be a useful and viable tool in building data driven models of non-linear processes.</p>10aequality constraints10aLS-SVM10amodel selection10aOptimization10apattern search1 aPatil, N., S.1 aShelokar, P., S.1 aJayaraman, Valadi, K.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/regression-models-using-pattern-search-assisted-least-square-support-vector-machines-0