A　set　membership　estimation　method　by　weighted　least　squares　support　vector　machines　(LS-SVM)　was　proposed　for　nonlinear-in-parameter　regression　models　with　unknown　but　bounded　errors.　A　weighted　least　squares　support　vector　regression　(LS-SVR)　was　solved　to　build　a　model　which　approximated　the　complex　functional　relationship　between　the　weighted　l∞　norms　of　the　equation-error　vectors　and　the　given　parameter　vectors.　Then　the　approximate　feasible　parameter　set　was　obtained　according　to　this　model　and　the　feasible　weighted　l∞　norms　of　the　equation-error　vectors.　In　order　to　evaluate　the　results　of　the　proposed　method,　an　index　reflecting　the　closeness　between　the　approximate　boundary　and　the　true　boundary　was　given.　The　simulation　results　show　that　the　proposed　method　can　give　approximate　boundaries　much　closer　to　true　boundaries　than　the　method　by　unweighted　LS-SVM.