TRAINDIFFEVOL(NET,Pd,Tl,Ai,Q,TS,VV) takes these inputs,

　　 NET - Neural network.

　　 Pd - Delayed input vectors.

　　 Tl - Layer target vectors.

　　 Ai - Initial input delay conditions.

　　 Q - Batch size.

　　 TS - Time steps.

　　 VV - Either empty matrix [] or structure of validation vectors.

　　 and returns,

　　 NET - Trained network.

　　 TR - Training record of various values over each epoch:

　　 TR.epoch - Epoch number.

　　 TR.perf - Training performance.

　　 TR.vperf - Validation performance.

　　 TR.tperf - Test performance.

　　 TR.mu - Adaptive mu value.-TRAINDIFFEVOL(NET,Pd,Tl,Ai,Q,TS,VV) takes these inputs,

　　　　NET - Neural network.

　　　　Pd　- Delayed input vectors.

　　　　Tl　- Layer target vectors.

　　　　Ai　- Initial input delay conditions.

　　　　Q　 - Batch size.

　　　　TS　- Time steps.

　　　　VV　- Either empty matrix [] or structure of validation vectors.

　　　and returns,

　　　　NET - Trained network.

　　　　TR　- Training record of various values over each epoch:

　　　　　　　TR.epoch - Epoch number.

　　　　　　　TR.perf　- Training performance.

　　　　　　　TR.vperf - Validation performance.

　　　　　　　TR.tperf - Test performance.

　　　　　　　TR.mu　　- Adaptive mu value.