From　the　magneto-elastic　effect　principle,　it　is　known　that　diversity　exists　in　the　hysteresis　loops　of　rode-like　ferromagnetic　material　under　different　tension.　So　it　can　be　employed　to　develop　an　improved　method　for　tension　estimation　of　rode-like　structure.　Firstly,　the　hysteresis　loop　signals　of　rod-like　structure　are　acquired　by　an　EM　sensor　which　is　composed　of　two　coaxial　solenoid　coils.　Then,　a　curve　of　hysteresis　loop　change　(CHLC)　is　defined　to　reflect　the　tension　influence　on　each　point　of　the　hysteresis　loop.　Secondly,　low-resolution　CHLC　is　obtained　by　using　wavelet　analysis.　Finally,　neural　network　is　used　to　establish　the　relationship　between　the　low-resolution　CHLC　and　tension　after　the　data　with　different　tension　is　used　to　train　the　neural　network.　The　experimental　results　show　that　CHLC　can　reflect　the　tension　influence　on　each　point　of　hysteresis　loop　intuitively.　The　low-resolution　CHLC　not　only　has　the　characteristics　of　including　the　entire　information　of　tension　but　also　has　the　characteristics　of　low　dimensionality.　The　relationship　between　the　low-resolution　CHLC　and　tension　can　be　obtained　by　using　neural　network,　without　analysis　of　the　sensitivity　curve　and　coefficient　of　determination　curve.　The　simple　linear　interpolation　based　RBF　neural　network　has　a　better　performance　than　BP　neural　network　and　RBF　neural　network.　The　tension　measurement　method　based　on　low-resolution　CHLC　and　simple　linear　interpolation　based　RBF　neural　network　is　applied　to　two　coaxial　solenoid　coils　based　EM　sensor　to　measure　the　tension　and　its　average　prediction　error　and　coefficient　of　determination　are　0.11%　and　1,　respectively.　This　method　is　effective　and　can　meet　the　actual　measurement　requirements.　©　2017,　Science　Press.　All　right　reserved.