1. Neuro-DEVS An hybrid methodology to describe complex systems
Jean-Baptiste Filippi
Paul Bisgambiglia
Marielle Delhom
SPE Laboratory
University of Corsica
UMR CNRS 6134

2. Introduction Modeling and simulation of complex systems
Our aim is to define an environment based on :
OOMS, Object Oriented Modeling and Simulation technique, based on DEVS, can map to physical behavior that is well known
Neural Networks, are used to approximate a system behavior that lacks some understanding.
GIS to manage data

3. Introduction Hybrid methodology, coupling OOMS,
Neural Networks and GIS
Gain from the different approaches :
Offer a well defined framework for modeling
Improve model accuracy
Handle partial lack of system understanding
Create adaptive models (models that can learn)
Effective data management for ecosystem modeling

4. Based on DEVS formalism
Theory OOMS
Based on DEVS formalism
Allows to describe a complex system in a modular and hierarchical way
OOMS extends DEVS with 2 hierarchies :
Time hierarchy, (granularity).
Abstraction hierarchy (decomposition).
Atomic models
4 characteristic functions :
External transition
Output
Internal transition
Time advance

5. Theory Neural Networks
A neural network is a computational method inspired by studies of the brain and nervous systems in biological organisms.
A neural network is composed of interconnected processing elements (neurons) working in unison to solve a specific problem.

6. Theory ANN, Multi layered perceptron
A multi-layered perceptron is a neural network.
Matches most of our needs in eco-modeling, able to perform :
Function interpolation (time series prediction …)
Pattern recognition (OCR …)
Bounded I/O {-1,1}
Learning :
Pass 1, propagation
Inputs are sent forward
thought the weighted
Links
Pass 2, retro-propagation
The difference between
the actual and the desired
output is calculated and
sent backward to modify
the link’s weights.

7. Coupling NN & OOMS Purposes
Limitations of ANNs and OOMS
ANNs are "black boxes", hard to avoid unexpected behavior
OO model cannot adapt its behavior runtime
System has to be well understood before it can be modeled using OO techniques
3 Main applications of Neuro-DEVS
Adaptive models (models that can learn)
Concurrent simulation (real-time ANN results validation)
ANN as sub component (handle lack of understanding in sub-components)

8. Coupling NN & OOMS Purposes
Adaptive models
Ann sub components
Concurrent simulation

9. Coupling NN & OOMS NN abstraction
ANN designed by expert for specific purpose
Trained ANNs stored in libraries
ANN Object loaded at the initialization
ANN are trained on discrete data
Input and output messages are translated
into activations by the Neuro model

10. Coupling NN & OOMS Neuro-atomic
3 characteristic functions for the Neuro atomic model :
double learn(input_set, output_set)
void activate(input_set)
output_set propagate(input_set)
2 I/O functions
void setInputBounds(min_set, max_set)
void setOutputBounds(min_set, max_set)

11. Coupling NN & OOMS Neuro-atomic
Simulation
activate() external transition
propagate() output function
learn() internal transition

12. Coupling NN & OOMS Interface
Neural network Object
accessed thought an
IDL interface.
Any ANN can be
accessed if it
implements the
proposed interface

13. Environment Simulation engine
Part of JDEVS toolkit
Developed in Java
Run on several hardware
Easy connection with Objects Brokers
Easy use of Threads
Easy distributed simulation
Slow

14. Environment M&S GUI Single java package Use of Swing
Simple flow chart editing
Drag’n Drop
Save in XML
Independent from the engine (can be used with high performing DEVS simulator)

15. Environment Collaborative
Supports library description of F.Bernardi.
Every coupled models are different XML files. edited on different clients to be federated later by the chief modeler.
Client can be launched using an application server (Tomcat..)

16. Environment Geographic Information System connection
Cellular Simulator can load initialization data from maps that has been exported from a GIS
Ouput messages are recomposing time maps that can be imported in the GIS

17. Environment JDEVS ToolKit
JDEVS Toolkit composed of 5 independent modules :
Simulation engine, (Bisgambiglia, Filippi)
The library for models storage (Bernardi)
The graphic modeling interface (Filippi) 
The cellular simulation (Filippi)
The GIS connector (Filippi, Chiari)
The five modules are independent
The aim is to provide an easy to use modeling package (especially for ecosystem modeling)

18. Application, Energy Farms
An energy farm is a renewable energy power plant (such as photovoltaic or wind turbine)

19. Application, Energy Farms ANN battery damage model
The battery is a power tank
The neural network is calculating the capacity loss of the battery for each discharge.
The capacity loss is function of the discharge length and deepness.
To simulate the behavior of another battery, it is necessary to train the neural net with collected data.
Battery
Discharge length (time)
Capacity loss
Discharge deepness (% charge)

20. Application, Energy Farms Implementation
Developed in 5
hours by expert.
Modeling and
Simulation trought
the GUI.
100 seconds
simulation time
(P3 800Mhz, messages)

21. Application, Energy Farms Some results

22. Conclusion
Neuro DEVS can help where system behavior is not understood and data available
Results are goods, compared to real world data, but more empirical data has to be collected to provide a realistic damage model
OO Environment is a fast and efficient modeling toolkit but needs more coherence

23. Conclusion Future works
Test on several models, JDEVS already been successfully used for M&S of
Bugs propagation in orchards
CORBA dialog simulation
PV system
Fire propagation
Model auto optimization module based on Genetic Algorithms (ex. to find the best battery and the best solar panel size for a specific place)
Expert system for modeling
High performance simulation kernel