Introduction Parallel Genesis allows a modeler to distribute a simulation across a compuational platform that supports the PVM message passing library. This includes most supercomputers and workstation networks. There is a natural mapping from network-level models to a parallel computer, but Parallel Genesis is also capable of executing a single-cell model on a parallel platform. This document describes the language extensions for parallel programming in the Genesis script language. The first section introduces the programming model and the second section specifies the syntax and semantics of each new script language extension.
Neural Network Based System Identification Toolbox 20
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Department of Automation, Building 326, 2800 Lyngby, Denmark, Technical University of Denmark
The toolbox contains a number of m-files for training and evaluation of multi-layer perceptron type neural networks. There are functions for working ordinary feed-forward networks as well as for identification of nonlinear dynamic systems and time-series analysis. Not all functions have been thoroughly tested yet and even if I haven*t been able to find any bugs, I can*t guarantee that you won*t encounter any problems.
Below an overview of the files contained in this directory is given along with a brief description of what they do. The on-line help facility explains how to call the different functions. You simply write help in the MATLAB command window. Along with the m- and c-files in this directory you will find a manual for the toolbox. Start by printing this out and read the release notes. A number of simple demonstration programs have been made to illustrate how most of the functions work. Run these to get an idea of what the toolbox provides. Be careful with the demos, though. Due to the existence of several local minimas, the results will often vary a great deal from run to run.
MPIL is an instance-based learning system (instances are simply viewed as points in n-dimensional real-space with an associated neighborhood), which utilizes two models for creating neighborhoods. The first model (i.e., MPIL-1) places a single neighborhood sphere (based on Euclidean distance measure) around an instance, and is in nature similar to the nearest neighbor classifier, except that it removes redundant instances.
The second model (i.e., MPIL-2) incorporates N radii (one for each input of an instance). This model also supports knowledge acquisition in the form of rule extraction. In a sense, both approaches are similar to neural networks in that they exploit a very similar parallelism. MPIL represents a good alternative in cases were large amounts of data have to be learned and provides good facilities for storage reduction.