In　order　to　overcome　the　defects　of　high　complexity　and　probability　of　falling　into　the　local　minimum　occurred　in　the　present　neural　network　(NN)　based　predistortion　methods,　a　new　predistortion　method　based　on　real-valued　focused　time-delay　fully　connected　cascade　neural　network　(RVFTDFCCNN)　trained　by　the　joint　algorithm　of　orthogonal　differential　evolution　krill　herd　(ODEKH)　and　neuron-by-neuron　(NBN)　was　proposed.　Both　the　predistorter　and　the　inverse　estimator　in　the　predistortion　system　were　modeled　by　RVFTDFCCNN.　The　initial　parameters　of　RVFTDFCCNN　were　first　obtained　by　the　ODEKH　algorithm　with　strong　global　search　ability.　Then　the　RVFTDFCCNN　was　further　trained　by　NBN　algorithm.　According　to　the　chain　rule　of　complex　function　partial　derivative,　the　calculation　of　the　Jacobian　matrix　in　the　NBN　algorithm　was　optimized　at　two　levels.　In　the　simulation,　the　DTMB　wideband　signal　was　used　as　the　input　signal　of　the　predistortion　system.　The　simulation　results　showed　that　with　the　same　requirement　of　the　training　error　and　the　generalization　error,　the　computation　amount　of　the　NBN　algorithm　in　training　RVFTDFCCNN　was　considerably　reduced　than　that　in　SHL　neural　network.　The　ODEKH　algorithm　converged　faster　than　the　traditional　krill　herd　algorithm.　The　joint　ODEKH-NBN　training　algorithm　had　higher　training　accuracy　than　the　Levenberg-Marquardt(LM)　training　algorithm　by　one　amplitude　order,　and　outperformed　the　latter　algorithm　by　2　dB　in　terms　of　adjacent　channel　power　ratio　(ACPR)　of　the　HPA　output　signal　after　predistortion.　Thus　it　was　concluded　that　the　proposed　predistortion　method　owned　low　complexity　and　good　predistortion　performance.　©　2016,　Editorial　Department　of　Journal　of　Sichuan　University　(Engineering　Science　Edition).　All　right　reserved.