1995/01/19 by Ralph Sinkus · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Algorithm #Artificial intelligence #Artificial neural network #Biology #Computer science #Control engineering #Engineering #Error function #Feed forward #Feedforward neural network #Function (biology) #Image Processing Techniques and Applications #Industrial Vision Systems and Defect Detection #Minification #Neural Networks and Applications #hep-ex
paper · pdf · doi:10.1016/0168-9002(95)00247-2
published as Nucl.Instrum.Meth.A361:290-296,1995 · 11 pages, latex, 3 figures appended as uuencoded file
arxiv created 1995/01/19 · openalex publication_date 1995/07/01 · arxiv updated 2010/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Feedforward neural networks with error backpropagation (FFBP) are widely applied to pattern recognition. One general problem encountered with this type of neural networks is the uncertainty, whether the minimization procedure has converged to a global minimum of the cost function. To overcome this problem a novel approach to minimize the error function is presented. It allows to monitor the approach to the global minimum and as an outcome several ambiguities related to the choice of free parameters of the minimization procedure are removed.