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Java in the Cloud: Rapidly develop and deploy Java business applications in the cloud. Java EE—the Most Lightweight Enterprise Framework? The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. ANNs modeler along the course of ANN project development. The history of the evolution of neurocomputing and its relation to the field of neurobiology is briefly discussed. ANNs are compared to both expert systems and statistical regression and their advantages and limitations are outlined. A generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation, is described.
The most common problems that BPANNs developers face during training are summarized in conjunction with possible causes and remedies. The developed model was reasonably accurate in simulating both training and test time-dependent growth curves as affected by temperature and pH. Check if you have access through your login credentials or your institution. 2000 Published by Elsevier B. Sorry, preview is currently unavailable.
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