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114
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WSC
2000
15 years 3 months ago
Model abstraction for discrete event systems using neural networks and sensitivity information
STRACTION FOR DISCRETE EVENT SYSTEMS USING NEURAL NETWORKS AND SENSITIVITY INFORMATION Christos G. Panayiotou Christos G. Cassandras Department of Manufacturing Engineering Boston ...
Christos G. Panayiotou, Christos G. Cassandras, We...
98
Voted
BMCBI
2007
141views more  BMCBI 2007»
15 years 2 months ago
Artificial neural network models for prediction of intestinal permeability of oligopeptides
Background: Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the inte...
Eunkyoung Jung, Junhyoung Kim, Minkyoung Kim, Dong...
ENGL
2007
148views more  ENGL 2007»
15 years 2 months ago
A General Reflex Fuzzy Min-Max Neural Network
—“A General Reflex Fuzzy Min-Max Neural Network” (GRFMN) is presented. GRFMN is capable to extract the underlying structure of the data by means of supervised, unsupervised a...
Abhijeet V. Nandedkar, Prabir Kumar Biswas
NN
2007
Springer
15 years 1 months ago
Recurrent neural network modeling of nearshore sandbar behavior
The temporal evolution of nearshore sandbars (alongshore ridges of sand fringing coasts in water depths less than 10 m and of paramount importance for coastal safety) is commonly ...
Leo Pape, B. Gerben Ruessink, Marco A. Wiering, Ia...
122
Voted
IJCNN
2006
IEEE
15 years 8 months ago
Adaptation of Artificial Neural Networks Avoiding Catastrophic Forgetting
— In connectionist learning, one relevant problem is “catastrophic forgetting” that may occur when a network, trained with a large set of patterns, has to learn new input pat...
Dario Albesano, Roberto Gemello, Pietro Laface, Fr...