3 edition of Robustness enhancement of neurocontroller and state estimator found in the catalog.
Robustness enhancement of neurocontroller and state estimator
by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC], [Springfield, Va
Written in English
|Series||NASA technical memorandum -- 106028.|
|Contributions||United States. National Aeronautics and Space Administration.|
|The Physical Object|
We performed whole-cell patch-clamp recordings of layer 2/3 cortical neurons in awake mice (n = total neurons from 38 animals) while monitoring treadmill motion, whisking behavior, eye movements, and pupil diameter (Figures 1A and 1B).All analyses in Figures 1, 2, and 3 were of spontaneous recordings, in order to avoid any confounding effects of visual by: Quasi-stationary state estimators 7 Conclusions and Open Questions 7 Robust Control of Large Power Systems via Convex Optimization 1 Introduction 2 Exciter Control Design using Linear Matrix Inequalities 3 Some Simulation Results 4 New Research Directions .
Vehicle dynamics are directly dependent on tire-road contact forces and torques which are themselves dependent on the wheels’ load and tire-road friction characteristics. An acquisition of the road disturbance property is essential for the enhancement of vehicle suspension control systems. This paper focuses on designing an adaptive real-time road profile estimation observer considering load Cited by: A recursive robust state estimator is originally derived through sensitivity penalization in  and extended in  to linear systems with intermittent data arrivals. The Riemannian distance between two positive definite matrices was first introduced in  for studying the asymptotic properties of .
for any initial state, evolution, measurement procedure and estimator, if the latter is consistent and unbiased. The quantity is the quantum Fisher information (QFI) of the probes' state with respect to ω after the encoding and, indeed, it fixes the ultimate achievable precision; the CRB can be saturated in the limit of an infinite number of repetitions. Current state-of-the-art Neural MT systems are performing well but they are not robust enough. When the input is noisy, the quality of the output drops drastically. In this blog, we will take a look at the impact of various types of noises on the quality and we will discuss techniques proposed by Vaibhav et al. () to improve the robustness of an NMT system.
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Get this from a library. Robustness enhancement of neurocontroller and state estimator. [Terry Troudet; United States. National Aeronautics and Space Administration.]. Robustness enhancement of neurocontroller and state estimator. By Terry Troudet.
Abstract. The feasibility of enhancing neurocontrol robustness, through training of the neurocontroller and state estimator in the presence of system uncertainties, is investigated on the example of a multivariable aircraft control problem.
The performance and Author: Terry Troudet. The feasibility of enhancing neurocontrol robustness, through training of the neurocontroller and state estimator in the presence of system uncertainties, is investigated on the example of a multivariable aircraft control by: 2.
To enhance the robustness of a power system state estimator to topology errors, bad critical measurements, multiple non-interacting, or interacting bad dat.
Enhanced Robustness of State Estimator to Bad Data Processing Through Multi-innovation Analysis - IEEE Journals & Magazine. Skip to Main Content. IEEE XploreDigital by: CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract—We are interested in training neurocontrollers for robustness on discrete-time models of physical systems.
Our neurocontrollers are implemented as recurrent neural networks (RNNs). A model of the system to be controlled is known to the ex-tent of parameters and/or signal uncertainties. Robustness facilitates evolvability and robust traits are often selected by evolution.
Such a mutually beneficial process is made possible by specific architectural features observed in robust. The Robustness of Estimator Composition. Part of: Advances in Neural Information Processing Systems 29 (NIPS ) Authors. Pingfan Tang; Jeff M. Phillips; Conference Event Type: Poster Abstract.
We formalize notions of robustness for composite estimators via the Author: Pingfan Tang, Jeff M. Phillips. robustness of neural networks involves the quixotic venture of testing all the possible failures, on all the possible inputs, which ultimately hits a combinatorial explosion for the rst, and theFile Size: 1MB.
The feasibility of enhancing neurocontrol robustness, through training of the neurocontroller and state estimator in the presence of system uncertainties, is investigated on the example of a. Towards Evaluating the Robustness of Neural Networks Nicholas Carlini David Wagner University of California, Berkeley ABSTRACT Neural networks provide state-of-the-art results for most machine learning tasks.
Unfortunately, neural networks are vulnerable to. The proposed estimator is compared with conventional weighted least squares (WLS) state estimator on basis of time, accuracy and robustness. It is observed that the time taken by the proposed.
The plant P() eventually converged to another equilibrium point than zero because of its nonlinear effect. Figure 4 shows the responses of the state variables from t = to t == for the plant P().
^^j^^^^ 92OO P TIME STEP ^ Figure 4. Responses of the state variables for P() by using the LOR by: 3. A Dynamic State Estimator Based Control for Power System Damping Abstract: Interarea power oscillation damping enhancement in large interconnected power systems is often accomplished using supplementary controls at the synchronous machines involved and with the use of flexible ac transmission system by: 3.
A new theory called 'Robust Adaptive Dynamic Programming' (for short, RADP) is developed for the design of robust optimal controllers for linear and nonlinear systems subject to both parametric.
A common requirement implicit in the current methods for the design of robust state estimators and robust fault detection filters is that the first Markov matrix must be non-zero, and indeed, full. Robustness has also been studied in more general contexts;  studies the connection between robustness and generalization,  establishes theoretical lower bounds on the robustness of linear and quadratic classiﬁers, and  seeks to improve robustness by promoting resiliance to.
Towards Evaluating the Robustness of Neural Networks Abstract: Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is similar to x but classified as by: Robust multiple model adaptive estimation for spacecraft autonomous navigation Aerospace Science and Technology, Vol.
42 An integrated estimation/guidance approach for seeker-less interceptorsCited by: In this study, the zonal mass streamfunction Ψ, which depicts intuitively the tropical Pacific Walker circulation (PWC) structure characterized by an enclosed and clockwise rotation cell in the zonal–vertical section over the equatorial Pacific, was used to study the changes of PWC spatial structure during –To examine the robustness of changes in PWC characteristics, the linear Cited by: Attack and estimator design for multi-sensor systems with undetectable the sending of the output measurements of the physical system to the estimator through communication networks and the estimator generates state estimates using the received sensor data.
He has authored or co-authored three books and over journal or conference Author: Haiyun Song, Peng Shi, Cheng-Chew Lim, Wen-An Zhang, Li Yu. El Niño–Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections.
This study uses simulations performed with coupled general circulation models (CGCMs) to investigate how regional precipitation in the twenty-first century may be affected by changes in both ENSO-driven precipitation variability and slowly evolving mean by: Abstract: We propose a novel method for noise power spectrum estimation in speech enhancement.
This method called extended-DATE (E-DATE) extends the d-dimensional amplitude trimmed estimator (DATE), originally introduced for additive white gaussian noise power spectrum estimation in “Robust estimation of noise standard deviation in presence of signals with unknown distributions and Cited by: The neurocontroller design uses the novel optimization neuro-dynamic programming algorithm based on dual heuristic programming (DHP), which has the most robust control capability among the adaptive critic designs family.
The radial basis function neural network (RBFNN) is used as the function approximator to implement the DHP by: