简介：Thispaperdescribestheinverstigationdevotedtoestablishsuitableweightsinafeed-forwardneuralnetworkrealizingthenarrow-bandfilteringmapinthecaseofadaptivelineenhancement(ALE)bytheutilityoftheoptimumcommonlearningratebackpropagation(OCLRBP)algorithm.Itisfoundthatafeed-forwardnetworkwith64linearinputandoutputneurons,and8oddsigmoidneuronsinthehiddenlayer,i.e.an(64→8→64)architecture,couldestablishthespecificinput-outputfunctioninthecaseofrelativelylowsignal-to-noiseradio.Onlyisaninputsignalconsistingofmixedperiodicandbroad-bandcomponentsavailabletothenetworksystem.Afterlearning,boththe"fanning-in-connectionpatterns",eachofwhichconsistsofweightsfanningintoahidden-neuronFromalltheoutputsofinput-neurons,andthe"fanning-out-connectionpatterns",eachofwhichconsistsofweightsfanningoutfromahidden-neurontoalltheinputsofoutput-neurons,aretunedtotheperiodicsignals.Thenonline

简介：

简介：

简介：合适的频率试验性地被选择是电磁的声学的变换器的操作频率。从钢和橡胶材料的一件多层的粘合剂样品的检测超声的回响的即时振幅，阶段和频率被计算并且创作了形成成功把严峻的信号与不同粘合剂状态品质上区分开来的三维的即时光谱。然后，在敏感时间窗户中的平均即时参数被拣起并且为BP人工的神经网络用作输入特徵向量。在训练并且测试体积的识别结果证明检测了电磁的超声的界面的回响能与比95%大的正确性比率自动地被识别并且分类。

简介：Thenetworkmethodformodelingthermoacousticenginesisdescribed.Somesimulationresultsonacousticfieldsandphasesinengine,especiallyinthethermoacousticstackarepresentedandanalyzed.Theeffectsofsomekeyfactorsonperformanceofstackandenginesystemaresimulatedanddiscussed.Theseeffectfactorsincludethespacesofplatesofstack,thepositionofstackinenginesystem,thesourceparameterofstack,andthemeanworkingpressureoftheenginesystem.