A Study of Practice Issues in Model-Based Verification Using the Symbolic Model Verifier (SMV)

1998 ◽  
Author(s):  
Grama R. Srinivasan ◽  
David P. Gluch
Keyword(s):  
Symbolic Model ◽  
Model Based

scholarly journals LEARNING AND UNDERSTANDING BASED ON NEURAL NETWORK TREES

2014 ◽  
pp. 84-92
Author(s):  
Qiangfu Zhao
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Tree ◽  
The Other ◽  
Symbolic Model ◽  
Changing Environments ◽  
Model Based ◽  
Black Boxes ◽  
Network Tree ◽  
Recognition Learning

Models for machine learning can be categorized roughly into two groups: symbolic and non-symbolic Generally speaking, symbolic model based learning can provide understandable results, but cannot adapt to changing environments efficiently. On the other hand, non-symbolic model based learning can adapt to changing environments, but the results are usually "black-boxes”. In our study, we introduced a hybrid model called neural network tree (NNTree). An NNTree is a decision tree (DT) with each non-terminal node containing an expert neural network (ENN). Results obtained so far show that an NNTree can be re-trained incrementally using new data. In addition, an NNTree can be interpreted easily if we restrict the number of inputs for each ENN. Thus, it is possible to perform recognition, learning and understand using the NNTree model alone.

Model-Based Variable and Transition Orderings for Efficient Symbolic Model Checking

2006 ◽  
pp. 524-540 ◽  
Author(s):  
Wendy Johnston ◽  
Kirsten Winter ◽  
Lionel van den Berg ◽  
Paul Strooper ◽  
Peter Robinson
Keyword(s):  
Model Checking ◽  
Symbolic Model Checking ◽  
Symbolic Model ◽  
Model Based
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