Advances in ComputersAcademic Press, 1991. gada 13. sept. - 325 lappuses Advances in Computers |
Saturs
Chapter 2 ObjectOriented Modeling and DiscreteEvent Simulation | 67 |
Chapter 3 HumanFactors Issues in Dialog Design | 115 |
Chapter 4 Neurocomputing Formalisms for Computational Learning and Machine Intelligence | 173 |
Chapter 5 Visualization in Scientific Computing | 247 |
Author Index | 307 |
Subject Index | 317 |
Contents of Previous Volumes | 327 |
Bieži izmantoti vārdi un frāzes
abstract component ACM SIGGRAPH adjoint algorithms application approach architecture Barhen behavior client command names complex computer graphics computer science concept concrete component constraints context coordinator coupled model data structure database defined DEVS formalism dialog discrete-event display Distributed Simulation domain dynamic system engineering environment equations example function Gulati Herr hierarchical Human-Computer Interaction IEEE implementation information hiding inheritance input Interaction interface inverse kinematics Item Lagrange multipliers learning manipulation mapping mathematical menu methods Natural Language neural networks neuromorphic neurons nonlinear object-oriented objects options output par-set parallel parameters performance potential problem Proc processing processors query languages representation requires reusable components reusable software reuse robot scientific visualization scientists Section software components software-components industry specification Stack Stack_Template supercomputer synaptic task techniques Technology terminal attractors testing theory tion Turner Whitted variables workstation Zeigler
Populāri fragmenti
76. lappuse - ... models requires that we adopt a different view than that fostered by traditional simulation languages. As with modular specification in general, we must view a model as possessing input and output ports through which all interaction with the environment is mediated. In the discrete event case, events determine values appearing on such ports. More specifically, when external events, arising outside the model, are received on its input ports, the model description must determine how it responds...
248. lappuse - Visualization embraces both image understanding and image synthesis. That is, visualization is a tool both for interpreting image data fed into a computer, and for generating images from complex multidimensional data sets. It studies those mechanisms in humans and computers which allow them in concert to perceive, use and communicate visual information.
305. lappuse - CM-5 was provided by the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign.
70. lappuse - Simulation is the process of designing a model of a real system and conducting experiments with this model for the purpose either of understanding the behavior of the system or of evaluating various strategies (within the limits imposed by a criterion or set of criteria) for the operation of the system.
77. lappuse - ... function has elapsed. • the external transition function which specifies how the system changes state when an input is received - the effect is to place the system in a new phase and sigma thus scheduling it for a next internal transition; the next state is computed on the basis of the present state, the input port and value of the external event, and the time that has elapsed in the current state. • the output function which generates an external output just before an internal transition...
74. lappuse - In general terms a model can be considered valid if the data generated by the model agree with the data produced by the real system in an experimental frame of interest. • The simulation relation, linking model and simulator, represents how faithfully the simulator is able to carry out the instructions of the model. There is a crucial element that has been brought into this picture: the experimental frame.
72. lappuse - Discrete event systems represent certain constellations of such parameters just as continuous systems do. For example, the inputs in discrete event systems occur at arbitrarily spaced moments, while those in continuous systems are piecewise continuous functions of time. The insight provided by the DEVS formalism is in the simple way that it characterizes how discrete event simulation languages specify discrete event system parameters. Having this abstraction, it is possible to design new simulation...