This　paper　proposes　a　constructing-and-pruning　(CP)　approach　to　optimise　the　structure　of　a　feedforward　neural　network　(FNN)　with　a　single　hidden　layer.　The　number　of　hidden　nodes　or　neurons　is　determined　by　their　contribution　ratios,　which　are　calculated　using　a　Fourier　decomposition　of　the　variance　of　the　FNN＇s　output.　Hidden　nodes　with　sufficiently　small　contribution　ratios　will　be　eliminated,　while　new　nodes　will　be　added　when　the　FNN　cannot　satisfy　certain　design　objectives.　This　procedure　is　similar　to　the　growing　and　pruning　processes　observed　in　biological　neural　networks.　The　performance　of　the　proposed　method　is　evaluated　using　a　number　of　examples:　real-life　date　classification,　dynamic　system　identification,　and　the　key　variables　modelling　in　a　wastewater　treatment　system.　Experimental　results　show　that　the　proposed　method　effectively　optimises　the　network　structure　and　performs　better　than　some　existing　algorithms.　(c)　2012　Elsevier　B.V.　All　rights　reserved.