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Full-Text Articles in Engineering
An Industrial Application To Neural Networks To Reusable Design, Donald C. Wunsch, R. Escobedo, T. P. Caudell, S. D. G. Smith, G. C. Johnson
An Industrial Application To Neural Networks To Reusable Design, Donald C. Wunsch, R. Escobedo, T. P. Caudell, S. D. G. Smith, G. C. Johnson
Electrical and Computer Engineering Faculty Research & Creative Works
Summary form only given, as follows. The feasibility of training an adaptive resonance theory (ART-1) network to first cluster aircraft parts into families, and then to recall the most similar family when presented a new part has been demonstrated, ART-1 networks were used to adaptively group similar input vectors. The inputs to the network were generated directly from computer-aided designs of the parts and consist of binary vectors which represent bit maps of the features of the parts. This application, referred to as group technology, is of large practical value to industry, making it possible to avoid duplication of design …
An Empirical Analysis Of Backpropagation Error Surface Initiation For Injection Molding Process Control, Alice E. Smith, Elaine R. Raterman, Cihan H. Dagli
An Empirical Analysis Of Backpropagation Error Surface Initiation For Injection Molding Process Control, Alice E. Smith, Elaine R. Raterman, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
Backpropagation neural networks are trained by adjusting initially random interconnecting weights according to the steepest local error surface gradient. The authors examine the practical implications of the arbitrary starting point on the error landscape of the ensuing trained network. The effects on network convergence and performance are tested empirically, varying parameters such as network size, training rate, transfer function and data representation. The data used are live process control data from an injection molding plant