In　this　paper,　a　novel　free　piston　expander-linear　generator　(FPE-LG)　prototype　has　been　developed　for　small　scale　organic　Rankine　cycle　system.　The　effects　of　three　key　operating　parameters,　including　intake　pressure,　operation　frequency　and　external　load　resistance　on　piston　dynamics,　output　characteristics　of　the　linear　generator,　and　system　energy　conversion　efficiency　are　investigated.　An　artificial　neural　network　(ANN)　based　prediction　model　is　established　after　evaluating　different　learning　rates,　hidden　layer　neural　numbers　and　train　functions.　The　ANN　model　is　also　validated　and　tested　using　the　experimental　data　with　consideration　of　mean　squared　error　and　correlation　coefficient.　Finally,　combined　the　genetic　algorithm　with　the　ANN　model,　a　parametric　optimization　and　performance　prediction　for　maximum　power　output　of　the　linear　generator　is　conducted.　The　results　show　that　the　free　piston　assembly　operates　stably　with　good　consistency.　Higher　intake　pressure　and　external　load　resistance　are　beneficial　for　improving　the　piston　dynamics　and　output　characteristics　of　the　linear　generator　while　the　optimal　operation　frequency　corresponding　to　the　maximum　peak　power　output　is　more　dependent　on　the　coordinated　variation　of　the　operating　parameters.　The　maximum　system　energy　conversion　efficiency　can　reach　up　to　28.81%　with　the　intake　pressure　of　0.2　MPa,　operation　frequency　of　1.5　Hz　and　external　load　resistance　of　5　Omega.　The　proposed　ANN　model　shows　a　strong　learning　ability　and　generalization　performance.　The　correlation　coefficients　between　the　ANN　predictions　and　experimental　data　obtained　from　the　validation　and　test　processes　are　all　close　to　1.　The　optimized　peak　power　output　can　reach　up　to　100.47　W　based　on　the　proposed　ANN　model.　The　ANN　based　method　can　provide　a　useful　guidance　for　the　performance　prediction　and　coordinated　optimization　with　the　least　deviation　and　high　accuracy.　(C)　2019　Elsevier　Ltd.　All　rights　reserved.