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#author("2017-03-17T11:56:43+09:00","default:Uedalab","Uedalab")
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''HydLa'' is a constraint-based [[hybrid system>http://en.wikipedia.org/wiki/Hybrid_system]] modeling language.
The key features of HydLa are:
- declarative (cf. procedural) modeling of systems as constraints over real-valued functions,
- description of models using familiar mathematical and temporal logic notations, and
- constraint hierarchies that enable concise modeling of default and exceptional behaviors.
''HyLaGI'', which is an implementation of HydLa, has been developed using C++ and
uses Mathematica and REDUCE as an external constraint solver. The features of
HyLaGI are:
- error-free simulatation using symbolic computation,
- simulation of system with symbolic parameters and uncertainties, and
- search for all solution trajectories using non-deterministic execution.
''HydLa'' is a modeling language for '''[[hybrid systems>http://en.wikipedia.org/wiki/Hybrid_system]]''', that is, dynamical systems involving both continuous and discrete changes.
// #ref(../roof_bouncing.png)
#ref(../bouncing_particle.png,75%)
The key features of HydLa are:
- '''declarative''' modeling of systems as constraints over real-valued functions and
// - model description using mathematical and logical notations, and
- '''constraint hierarchies''' that enable concise modeling of default and exceptional behaviors.
We are also developing ''HIDE'', an integrated development environment for HydLa.
HIDE allows us to model, simulate and visualize models within a unified environment.
To get started with HydLa, visit ''[[webHydLa>http://webhydla.ueda.info.waseda.ac.jp]]'', an online IDE of HydLa with a visualizer, and run example programs.
''[[webHydLa]]'' is a web interface of HydLa.
[[webHydLa]] has the same features as HIDE.
To run HydLa programs on your computer, a simulation engine of HydLa, named ''[[HyLaGI]]'', can be downloaded. Unlike many other simulation engines, HyLaGI provides the following key features:
- '''error-free simulation''' using symbolic computation,
- simulation of systems with symbolic parameters that allow us to represent uncertainties (such as 0.9 < p < 1.1), and
- search for all solution trajectories using nondeterministic execution,
#br
For example, the following trajectories were computed from a bouncing ball model whose initial height is given as a symbolic parameter ranging between 0 and 10.
// #ref(../roof_bouncing.png)
#ref(../bouncing_ball_webHydLa.png,75%)
