A type of wavelet neural network, in which the scale function isadopted only, is proposed in this paper for non-linear dynamicprocess modelling. Its network size is decreased significantly andthe weight coefficients c...A type of wavelet neural network, in which the scale function isadopted only, is proposed in this paper for non-linear dynamicprocess modelling. Its network size is decreased significantly andthe weight coefficients can be estimated by a linear algorithm. Thewavelet neural network holds some advantages superior to other typesof neural networks. First, its network structure is easy to specifybased on its theoretical analysis and intuition. Secondly, networktraining does not rely on stochastic gradient type techniques andavoids the problem of poor convergence or undesirable local minima.展开更多
The bulk TC17was subjected to the high energy shot peening(HESP)at the air pressures ranging from0.35to0.55MPa and processing durations ranging from15to60min.The microhardness(HV0.02)from topmost surface to matrix of ...The bulk TC17was subjected to the high energy shot peening(HESP)at the air pressures ranging from0.35to0.55MPa and processing durations ranging from15to60min.The microhardness(HV0.02)from topmost surface to matrix of the HESP processed TC17was measured,which generally decreases with the increase of depth from topmost surface to matrix and presents different variation with air pressure and processing duration at different depths.A fuzzy neural network(FNN)model was established to predict the surface layer microhardness of the HESP processed TC17,where the maximum and average difference between the measured and the predicted microhardness were respectively8.5%and3.2%.Applying the FNN model,the effects of the air pressure and processing duration on the microhardness at different depths were analyzed,revealing the significant interaction between the refined layer shelling and the continuous grain refinement.展开更多
基金Supported by the Eu Information Technologies Programme Project(No. 22416) and National High Tech R&D Project(863/Computer Integrated Manufacture System AA413130) of China.
文摘A type of wavelet neural network, in which the scale function isadopted only, is proposed in this paper for non-linear dynamicprocess modelling. Its network size is decreased significantly andthe weight coefficients can be estimated by a linear algorithm. Thewavelet neural network holds some advantages superior to other typesof neural networks. First, its network structure is easy to specifybased on its theoretical analysis and intuition. Secondly, networktraining does not rely on stochastic gradient type techniques andavoids the problem of poor convergence or undesirable local minima.
基金Project (51475375) supported by the National Natural Science Foundation of China
文摘The bulk TC17was subjected to the high energy shot peening(HESP)at the air pressures ranging from0.35to0.55MPa and processing durations ranging from15to60min.The microhardness(HV0.02)from topmost surface to matrix of the HESP processed TC17was measured,which generally decreases with the increase of depth from topmost surface to matrix and presents different variation with air pressure and processing duration at different depths.A fuzzy neural network(FNN)model was established to predict the surface layer microhardness of the HESP processed TC17,where the maximum and average difference between the measured and the predicted microhardness were respectively8.5%and3.2%.Applying the FNN model,the effects of the air pressure and processing duration on the microhardness at different depths were analyzed,revealing the significant interaction between the refined layer shelling and the continuous grain refinement.
