Controlling a truck with an adaptive critic CMAC design

Article Abstract:

In this study, CMAC (Cerebellar Model Articulated Controller) neural architectures are shown to be viable for the purposes of real-time learning and control. An adaptive critic neuro-control design has been implemented that learns in real-time how to back up a trailer truck along a fixed straight line trajectory. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. (Reprinted by permission of the publisher.)

Processor architectures, Industrial research, Neural networks, Motor vehicles, Product introduction, Real time systems, Research and Development, Software Design, Real-Time System, Scientific Research, Processor Architecture, Controllers, New Technique, Computer Learning, Neural Network

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Guidelines for the design of data driven generic simulators for specific domains

Article Abstract:

Data-driven generic simulators have, for many years, been used to model a wide range of systems. Some such simulators are promoted as general purpose, while others are specific to particular problem domains. This paper considers how domain-specific data-driven simulators should be constructed using a modular approach which allows the simulator to be incrementally enhanced and ported easily to alternative computing environments. It also argues that generic simulators will not wholly replace simulation programming for specific applications. (Reprinted by permission of the publisher.)

Systems analysis, Manufacturing, System Design, Tutorial, Simulation of Computer Systems, Dataflow Architecture

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