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- Global Positioning System (2)
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- Classification network (1)
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- Digital phase-locked loop filter (1)
- Fault-tolerant training (1)
- Fuzzy PLL (1)
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- GPS measurements (1)
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- Geometric dilution of precision (1)
- Gradient descent method (1)
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- Inertial navigation (1)
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- Navigation accuracy (1)
- Navigation satellite selection (1)
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- Satellite/vehicle geometry (1)
- Sinusoidal signal (1)
- Time-varying phase (1)
Articles 1 - 7 of 7
Full-Text Articles in Engineering
An Algorithmic Approach To Loop Shaping With Applications To Self-Tuning Control Systems, Zhiqiang Gao
An Algorithmic Approach To Loop Shaping With Applications To Self-Tuning Control Systems, Zhiqiang Gao
Electrical and Computer Engineering Faculty Publications
An algorithmic approach to feedback control design is introduced. It simplifies the existing iterative design process, which is often tedious, by reducing the design problem to solving a set of linear algebraic equations. The algorithmic nature of such an approach makes it attrative to not only off-line designs but also self-tuning control systems, where the compensators are continuously tuned on-line as the dynamics of the physical process vary with time. This is demonstrated in the example where the proposed algorithm is implemented for an industrial tension regulation system with successful simulation results. Extensions of the algorithm to …
Fault Tolerant Training For Optimal Interpolative Nets, Daniel J. Simon, Hossny El-Sherief
Fault Tolerant Training For Optimal Interpolative Nets, Daniel J. Simon, Hossny El-Sherief
Electrical and Computer Engineering Faculty Publications
The optimal interpolative (OI) classification network is extended to include fault tolerance and make the network more robust to the loss of a neuron. The OI net has the characteristic that the training data are fit with no more neurons than necessary. Fault tolerance further reduces the number of neurons generated during the learning procedure while maintaining the generalization capabilities of the network. The learning algorithm for the fault-tolerant OI net is presented in a recursive formal, allowing for relatively short training times. A simulated fault-tolerant OI net is tested on a navigation satellite selection problem
Navigation Satellite Selection Using Neural Networks, Daniel J. Simon, Hossny El-Sherief
Navigation Satellite Selection Using Neural Networks, Daniel J. Simon, Hossny El-Sherief
Electrical and Computer Engineering Faculty Publications
The application of neural networks to optimal satellite subset selection for navigation use is discussed. The methods presented in this paper are general enough to be applicable regardless of how many satellite signals are being processed by the receiver. The optimal satellite subset is chosen by minimizing a quantity known as Geometric Dilution of Precision (GDOP), which is given by the trace of the inverse of the measurement matrix. An artificial neural network learns the functional relationships between the entries of a measurement matrix and the eigenvalues of its inverse, and thus generates GDOP without inverting a matrix. Simulation results …
Fuzzy Logic For Digital Phase-Locked Loop Filter Design, Daniel J. Simon, Hossny El-Sherief
Fuzzy Logic For Digital Phase-Locked Loop Filter Design, Daniel J. Simon, Hossny El-Sherief
Electrical and Computer Engineering Faculty Publications
The problem of robust phase-locked loop design has attracted attention for many years, particularly since the advent of the global positioning system. This paper proposes and demonstrates the use of a fuzzy PLL to estimate the time-varying phase of a sinusoidal signal. It is shown via simulation results that fuzzy PLL's offer performance comparable to analytically derived PLL's (e.g. Kalman filters and H∞ estimators) when the phase exhibits high dynamics and high noise. The fuzzy PLL rules are optimized using a gradient descent method and a genetic algorithm
Gps Modeling For Designing Aerospace Vehicle Navigation Systems, John J. Dougherty, Hossny El-Sherief, Daniel J. Simon, Gary A. Whitmer
Gps Modeling For Designing Aerospace Vehicle Navigation Systems, John J. Dougherty, Hossny El-Sherief, Daniel J. Simon, Gary A. Whitmer
Electrical and Computer Engineering Faculty Publications
The complexity of the design of a Global Positioning System (GPS) user segment, as well as the performance demanded of the components, depends on user requirements such as total navigation accuracy. Other factors, for instance the expected satellite/vehicle geometry or the accuracy of an accompanying inertial navigation system can also affect the user segment design. Models of GPS measurements are used to predict user segment performance at various levels. Design curves are developed which illustrate the relationship between user requirements, the user segment design, and component performance.
Optimal Sequence Estimation For Convolutionally Coded Signals With Binary Digital Modulation In Isi Channels, Fuqin Xiong
Optimal Sequence Estimation For Convolutionally Coded Signals With Binary Digital Modulation In Isi Channels, Fuqin Xiong
Electrical and Computer Engineering Faculty Publications
Decoding convolutional codes with binary digital modulation in intersymbol interference (ISI) channels is studied. The receiver structure is a whitened matched filter (WMF) whose transfer function is determined by the ISI channel. Decoding of the output sequence can be performed in two steps or one step. The two-step decoding first decodes the ISI corrupted coded sequence back to the ISI free coded sequence which is then decoded back to the uncoded message sequence. For one-step decoding, the entire encoder-channel-receiver system is modeled as a new encoder with combined memory length of the memory lengths of the original …
Applying Neural Networks To Find The Minimum-Cost Coverage Of A Boolean Function, Pong P. Chu
Applying Neural Networks To Find The Minimum-Cost Coverage Of A Boolean Function, Pong P. Chu
Electrical and Computer Engineering Faculty Publications
To find a minimal expression of a boolean function includes a step to select the minimum cost cover from a set of implicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical for large input data size. Neural network approach is used to solve this problem. We first formalize the problem, and then define an ''energy function'' and map it to a modified Hopfield network, which will automatically search for the minima. Simulation of simple examples shows the proposed neural network can obtain good solutions most of the time.