A Model Making Automation Process (MMAP) Using a Graph Grammar Formalism

2000 ◽  
pp. 442-454 ◽  
Author(s):  
Curtis E. Hrischuk
Keyword(s):  
Graph Grammar

An Extension of Temporal Mechanism for Graph Grammar

2018 ◽  
Author(s):  
Zhan Shi ◽  
Jiande Zhang ◽  
Tingting Zhang ◽  
Xiaoqin Zeng ◽  
Dawei Li
Keyword(s):  
Graph Grammar

Converting metamodels to graph grammars: doing without advanced graph grammar features

2013 ◽  
Vol 14 (3) ◽  
pp. 1297-1317 ◽  
Author(s):  
Luka Fürst ◽  
Marjan Mernik ◽  
Viljan Mahnič
Keyword(s):  
Graph Grammar ◽  
Graph Grammars

Geometric Reasoning Based on Graph Grammar Parsing

1994 ◽  
Vol 116 (3) ◽  
pp. 763-769 ◽  
Author(s):  
Z. Fu ◽  
A. de Pennington
Keyword(s):  
Intelligent Design ◽  
Feature Recognition ◽  
Geometric Reasoning ◽  
Geometric Constraints ◽  
Graph Grammar ◽  
Feature Interaction ◽  
Knowledge Based ◽  
Feature Based ◽  
Feature Information ◽  
And Performance

It has been recognized that future intelligent design support environments need to reason about the geometry of products and to evaluate product functionality and performance against given constraints. A first step towards this goal is to provide a more robust information model which directly relates to design functionality or manufacturing characteristics, on which reasoning can be carried out. This has motivated research on feature-based modelling and reasoning. In this paper, an approach is presented to geometric reasoning based on graph grammar parsing. Our approach is presented to geometric reasoning based on graph grammar parsing. Our work combines methodologies from both design by features and feature recognition. A graph grammar is used to represent and manipulate features and geometric constraints. Geometric constraints are used within symbolical definitions of features constraints. Geometric constraints are used within symbolical definitions of features and also to define relative position and orientation of features. The graph grammar parsing is incorporated with knowledge-based inference to derive feature information and propagate constraints. This approach can be used for the transformation of feature information and to deal with feature interaction.

Transforming BPMN to BPEL based on Graph Grammar

2013 ◽  
Vol 8 (4) ◽  
pp. 1021-1027
Author(s):  
Zhan Shi ◽  
Xiaoqin Zeng ◽  
Yi Wang ◽  
Zhilong Zhen
Keyword(s):  
Graph Grammar

A Graph Grammar Approach to Generate Neural Network Topologies

2007 ◽  
Author(s):  
Chaitanya Vempati ◽  
Matthew I. Campbell
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Process Modeling ◽  
Graph Transformation ◽  
Graph Grammar ◽  
Transformation Rules ◽  
The Neural Network ◽  
Network Topologies ◽  
Novel Method ◽  
Defining Graph

Neural networks are increasingly becoming a useful and popular choice for process modeling. The success of neural networks in effectively modeling a certain problem depends on the topology of the neural network. Generating topologies manually relies on previous neural network experience and is tedious and difficult. Hence there is a rising need for a method that generates neural network topologies for different problems automatically. Current methods such as growing, pruning and using genetic algorithms for this task are very complicated and do not explore all the possible topologies. This paper presents a novel method of automatically generating neural networks using a graph grammar. The approach involves representing the neural network as a graph and defining graph transformation rules to generate the topologies. The approach is simple, efficient and has the ability to create topologies of varying complexity. Two example problems are presented to demonstrate the power of our approach.

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