Article Free Access Share on Measures of syntactic complexity for modeling behavioral VHDL Authors: Neal S. Stollon DSC Communications Corporation, Dallas, Texas DSC Communications Corporation, Dallas, TexasView Profile , John D. Provence Southern Methodist University, Electrical Engineering Dept., Dallas, Texas Southern Methodist University, Electrical Engineering Dept., Dallas, TexasView Profile Authors Info & Claims DAC '95: Proceedings of the 32nd annual ACM/IEEE Design Automation ConferenceJanuary 1995 Pages 684–689https://doi.org/10.1145/217474.217611Published:01 January 1995Publication History 1citation307DownloadsMetricsTotal Citations1Total Downloads307Last 12 Months2Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The use of artificial neural network is proposed for high-speed processing of rules in fuzzy logic controller (FLC). the logic element of an FLC is replaced by a single hidden layer feedforward network. the input and output fuzzy subsets are expressed it of numerical patterns. the network is trained using the back-propagation algori to establish fuzzy associations between the input and output fuzzy subsets. the inference mechanism of the network is compared with that of compositional law of inference. In the proposed implementation of FLC, all the rules are processed in paralle. This implementation has potential for high-speed processing of rules if the network is realized in hardware. the use of neural networks in fuzzy logic self-organizing is also ivestigated. © 1993 John Wiley & Sons, Inc.

The use of multilayer feedforward neural networks for self-organizing control of poorly defined dynamic processes is proposed. The control policy at any stage can be expressed in terms of linguistic control rules. These rules are treated quantitatively using fuzzy logic theory and implemented through neural networks to obtain deterministic control actions. The performance of the controller is evaluated at each time step using a performance measure, and the rules which contributed to previous control actions are reinforced. The neural network is then trained using backpropagation to accommodate such reinforcement in controller output. The performance of these controllers was tested through simulation on a number of linear dynamical systems as well as on nonlinear systems such as the cart-pole balancing problem. The results indicate that the controller has strong adaptive properties, and it can be built with only a little knowledge about the system dynamics. The performance of the controller was also observed to be highly robust to system parameter changes

The theory and design of systolic arrays for Viterbi processing in communication systems with a time-dispersive time-varying channel is discussed. The architecture, algorithms, and processor elements, for a two-dimensional systolic array are described. The array supports the branch metric computations required for an adaptive Viterbi processor. The array is designed so that computations propagate along the rows of the array, while data symbols propagate along the columns. All interprocessor data flow and connections within the array are nearest-neighbor. The array illustrates how the Viterbi-processor algorithms can be structured to achieve a high degree of computational concurrency. Variations in the array design are described and evaluated in terms of computational resource requirements and utilization and computational throughput. A high-bandwidth memory interface is proposed, and system design considerations are discussed.>