The development and construction of artificial intelligence to solve a plant layout design problem are considered. The heuristic for pattern allocation, the heuristic search and heuristic function for problem solving,...
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The development and construction of artificial intelligence to solve a plant layout design problem are considered. The heuristic for pattern allocation, the heuristic search and heuristic function for problem solving, and the plant design knowledge base are used to construct the intelligent system. The pattern allocation procedure is the heuristic for generating the alternative layouts. In the problem solving procedure, the search heuristic searches for the best layout from the candidate layouts and the heuristic function directs the search process in the most profitable direction. The system performance has been evaluated and tested many times.< >
A novel scheme for signature analysis based on cellular automata (CA) is proposed. The state transition behavior of such signature analyzers has been modeled by Markov chain. It has been shown that a special class of ...
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A novel scheme for signature analysis based on cellular automata (CA) is proposed. The state transition behavior of such signature analyzers has been modeled by Markov chain. It has been shown that a special class of such CAs achieves a steady-state aliasing probability lower than 1/2/sup n/ (for an n-cell CA) for specific ranges of input probabilities of the incoming error pattern. The dynamic behavior of linear feedback shift registers (LFSRs) has also been compared with CAs with the same characteristic polynomials. This work establishes the fact that CA-based signature analyzers outperform those based on LFSRs as regards both steady-state and dynamic behavior.< >
Numerical Weather Prediction (NWP) is acknowledged as being of vital importance to economy. The demand that NWP places on computing system performance has increased dramatically since the introduction of computer syst...
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First, an initial schedule that satisfies all timing constraints is generated using algorithms adapted from layout compaction. This schedule is then improved with respect to resource usage using simulated annealing. S...
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First, an initial schedule that satisfies all timing constraints is generated using algorithms adapted from layout compaction. This schedule is then improved with respect to resource usage using simulated annealing. SALSA provides for efficient exploration of alternative schedules while supporting timing constraints, conditionals, loops, and subroutines. The SALSA scheduling procedure is described in detail. An implementation of the approach shows promising results.< >
Static VAr compensators are reviewed for their application in transmission line compensation and load compensation. Emphasis is given to the application of thyristor controlled reactors and thyristor switched capacito...
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Static VAr compensators are reviewed for their application in transmission line compensation and load compensation. Emphasis is given to the application of thyristor controlled reactors and thyristor switched capacitors. After a quick review of the application of static VAr compensators, a detailed theoretical analysis of the reactive power requirement is made. A mathematical model of the compensator is established for the possible controllable parameters. In the theoretical analysis, both symmetrical line and radial line systems are considered. Stability of a transmission line is analyzed for reactive power compensation. Present trends are discussed for finer continuous reactive power control. Solid state reactive power compensation without the need for large capacitors and reactors is seen to be the most probable candidate. Solid state devices for high power capability and transient response along with other reactive power requirements are also discussed.< >
A form of adaptive model-based control is proposed and experimentally evaluated. An adaptive model-based neural network controller (AMBNNC) uses multilayer perceptron artificial neural networks to estimate the payload...
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A form of adaptive model-based control is proposed and experimentally evaluated. An adaptive model-based neural network controller (AMBNNC) uses multilayer perceptron artificial neural networks to estimate the payload during high-speed manipulator motion. The payload estimate adapts the feedforward compensator to unmodeled system dynamics and payload variation. The neural nets are trained through repetitive training on trajectory tracking error data. The AMBNNC was experimentally evaluated on the third link of a PUMA-560 manipulator. Tracking performance was evaluated for a wide range of payload and trajectory conditions and compared to a nonadaptive model-based controller. The superior tracking accuracy of the AMBNNC demonstrates the potential of the technique.< >
A class of gradient based adaptive algorithms is presented which employs order-statistical transformations of the gradient estimates over a short window. These algorithms, called order-statistical least mean squares (...
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A class of gradient based adaptive algorithms is presented which employs order-statistical transformations of the gradient estimates over a short window. These algorithms, called order-statistical least mean squares (OSLMS), are designed to facilitate adaptive filter performance close to the least-squares optimum in impulsive and other non-Gaussian input environments. Three specific OSLMS filters are defined: the median LMS, the averaged LMS, and the trimmed-mean LMS. For the median LMS some simple convergence results are given. Simulations of all three algorithms, conducted using a generalized exponential density, are presented.< >
We have developed a conceptual framework and a demonstration system that contextualize (or situate) learning in the context of real-world work situations. The conceptual framework is based on the following requirement...
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Unique localization of multiple sources by antenna arrays requires that the number of sensors be greater than the number of sources. When additional information is given this is no longer true. The authors assumed a s...
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Unique localization of multiple sources by antenna arrays requires that the number of sensors be greater than the number of sources. When additional information is given this is no longer true. The authors assumed a signal structure of the autoregressive (AR) type. A localization procedure is presented which consists of two stages; in the first stage the coefficients of the AR signals are estimated, while in the second stage the directions of arrival are estimated. Simulation results demonstrate the performance of the algorithm under different scenarios.< >
An improved version of the Papoulis algorithm for bandlimited signal interpolation is presented. This algorithm uses the concept of initial interpolation. The justification for initial interpolation is developed only ...
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An improved version of the Papoulis algorithm for bandlimited signal interpolation is presented. This algorithm uses the concept of initial interpolation. The justification for initial interpolation is developed only through experimental studies. It is shown that the performance of the interpolation scheme depends on the number and distribution of the known data samples.< >
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