Forward and reverse modeling of electron beam welding process using radial basis function neural networks

Ki:llc~r-ngptcr.??l 302, l~dirl .4bstract. An atrznlpt has k c n madr In ihr preqent i t u d ~ 10 rntdcl 111pur-oulpur relalir?nshi]~s ot'.ji~ clcclrt~r) k a m wclding prtkchs in both tunvard as well ss rcvcr.;u Cii~~ecti,)rl~ using radial haxi+ t'ur~cr~on nei~rsl nctwt>rks. The performance ot this ntt work is dcpcndenr on irs aruhiiecrurc wgr~iticat~rly, which. in lum. depzr~ds ,MI the number nf hiddcn neurons, as the n u m b u! input nodes and that of output neurl)n\ be decidcd hcir3rch~nd it rr modeling a par~ivi~lar process. l11pi11-ot~tput data can hr c(i15rcred bascd on t h e ~ r similarity among thet11. The number of hidden neurons of this nrluvrh i l generally kcpt alui~l 10 that t7i clusters made hy ihe &la-szl. Two popular f u ~ z y uluslerir~g algcrr~rhrns, namely U U L L ~ C'-tncal~\ and m~ropy-hascd furz! clusrering h a w k e n u s d for grouping t l ~ c data into some clu.c~rn. 4s hnth these algorithms hate rr~hel-e~lr limilations, a m;)dificd clustering slguritbm ha' bcm propcasd by eliminating rhrir demen 1s iitld combining their ;~dvnntages. Radial hnsis funsttam neural network devtlopd using the prop~xed clustering algorithm is found to perfr)rm M t e r th:in [hat dcsipntd h:~$ctl on the ahorc lwa we!l-kr~nu.~~ clvrrering algorithms.