Paul A. Fishwick

Simulation Model Design and Execution: Building Digital Worlds

From the Publisher: Simulation can be broken down into 3 areas. 1)Model Design 2)Model Execution and 3)Execution Analysis. Most Simulation books spend considerable time on #3 but very little on #1 and #2.

Time series forecasting using neural networks vs. Box- Jenkins methodology

We discuss the results of a comparative study of the performance of neural networks and conventional methods in forecasting time series. Our work was initially inspired by previously published works that yielded inconsistent results about comparative performance. We have experimented with three time series of different complexity using different feed forward, backpropagation neural network models and the standard Box-Jenkins model. Our experiments demonstrate that for time series with long memory, both methods produced comparable results. However, for series with short memory, neural networks outper formed the Box-Jenkins model. We note that some of the comparable results arise since the neural network and time series model appear to be functionally similar models. We have found that for time series of different complexities there are optimal neural network topologies and parameters that enable them to learn more efficiently. Our initial conclusions are that neural networks are robust and provide good long-term forecasting. They are also parsimonious in their data requirements. Neural networks represent a promising alternative for forecasting, but there are problems deter mining the optimal topology and parameters for efficient learning.

Web-based simulation: some personal observations

The web has a short history, but has grown at an exponential pace since its introduction six years ago. Web-based simulation represents the connection between the web and the field of simulation. Web based simulation is not an existing field but rather an idea which represents an interest on the part of simulationists to exploit web technology. To further this cause, I present some issues and concepts in web based simulation to serve as a back drop for a more formal discussion, and potentially the formation of a new simulation sub-area.

A multimodel methodology for qualitative model engineering

Qualitative models arising in artificial intelligence domain often concern real systems that are difficult to represent with traditional means. However, some promise for dealing with such systems is offered by research in simulation methodology. Such research produces models that combine both continuous and discrete-event formalisms. Nevertheless, the aims and approaches of the AI and the simulation communities remain rather mutually ill understood. Consequently, there is a need to bridge theory and methodology in order to have a uniform language when either analyzing or reasoning about physical systems. This article introduces a methodology and formalism for developing multiple, cooperative models of physical systems of the type studied in qualitative physics. The formalism combines discrete-event and continuous models and offers an approach to building intelligent machines capable of physical modeling and reasoning.

SimPack: getting started with simulation programming in C and C++

SimPack is a collection of C and C++ libraries and executable programs for computer simulation. In this collection, several different simulation algorithms are supported including discrete event simulation, continuous simulation and combined (multimodal) simulation. The purpose of the SimPack toolkit is to provide the user with a set of utilities that illustrate the basics of building a working simulation from a model description. We demonstrate that special purpose simulation programming languages can be easily constructed using language translation software with the SimPack utilities which act as the “assembly language.” We present several different dynamical system model forms and overview the methods used in SimPack for executing these models. SimPack includes some fairly extensive simulation facilities, and is in use by various instructors, researchers and industrial analysts for their modeling and simulation experiments.

Investigating ontologies for simulation modeling

Many fields have or are developing ontologies for their subdomains. The gene ontology (GO) is now considered to be a great success in biology, a field that has already developed several extensive ontologies. Similar advantages could accrue to the simulation and modeling community. Ontologies provide a way to establish common vocabularies and capture domain knowledge for organizing the domain with a community wide agreement or with the context of agreement between leading domain experts. They can be used to deliver significantly improved (semantic) search and browsing, integration of heterogeneous information sources, and improved analytics and knowledge discovery capabilities. Such knowledge can be used to establish common vocabularies, nomenclatures and taxonomies with links to detailed information sources. This paper investigates the use, the benefits and the development requirements of Web-accessible ontologies for discrete-event simulation and modeling. As a case study, the development of a prototype OWL-based ontology for modeling and simulation called the discrete-event modeling ontology (DeMO) is also discussed. Prototype ontologies such as DeMO can serve as a basis for achieving broader community agreement and adoption of ontologies for this field.

The role of process abstraction in simulation

The concept of process abstraction, which allows simulationists to construct models composed of a set of interconnected levels, is discussed. Each level in the network represents the process at some given level of abstraction and is encoded using a model type (e.g. Petri net, automaton, data flow graph) appropriate to that level. An example process composed of articulated figures around a circular table is presented. After the process is formally defined at each level, the abstraction relationships between levels are discussed. A taxonomy of process abstraction methods is presented in an effort to characterize the fundamental concepts of level traversal. The application involving the animation of the process is described within the context of the HIRES simulation language that was constructed specifically to simulate and analyze multilevel simulations. Textual and graphical examples of HIRES output are included. Finally, some observations on the future of process abstraction in modeling are given. >

Ontologies for modeling and simulation: issues and approaches

Ontologies represent the next important phase of the World Wide Web, creating a semantic Web which links together disparate pieces of information and knowledge. Creating ontologies within computer simulation can be seen as a logical next phase of the Web-based modeling and simulation thrust, where the emphasis is on knowledge and its representation rather than on run-time network characteristics. We introduce the concept of an ontology and then survey two groups performing research in this area at the Universities of Florida and Georgia, respectively.

Web-based simulation: revolution or evolution?

The nature of the emerging field of web-based simulation is examined in terms of its relationship to the fundamental aspects of simulation research and practice. The presentation, assuming a form of debate, is based on a panel session held at the first International Conference on Web-Based Modeling and Simulation, which was sponsored by the Society for Computer Simulation during 11-14 January 1998 in San Diego, California. While no clear “winner” is evident in this debate, the issues raised here certainly merit ongoing attention and contemplation.

Asynchronous parallel discrete event simulation

Complex models may have model components distributed over a network and generally require significant execution times. The field of parallel and distributed simulation has grown over the past fifteen years to accommodate the need of simulating the complex models using a distributed versus sequential method. In particular, asynchronous parallel discrete event simulation (PDES) has been widely studied, and yet we envision greater acceptance of this methodology as more readers are exposed to PDES introductions that carefully integrate real-world applications. With this in mind, we present two key methodologies (conservative and optimistic) which have been adopted as solutions to PDES systems. We discuss PDES terminology and methodology under the umbrella of the personal communications services application.