Analysis of fault-tolerant neurocontrol architectures

The fault-tolerance of analog parallel distributed implementations of a multivariable aircraft neurocontroller is analyzed by simulating weight and neuron failures in a simplified scheme of analog processing based on the functional architecture of the ETANN chip (Electrically Trainable Artificial Neural Network). The neural information processing is found to be only partially distributed throughout the set of weights of the neurocontroller synthesized with the backpropagation algorithm. Although the degree of distribution of the neural processing, and consequently the fault-tolerance of the neurocontroller, could be enhanced using Locally Distributed Weight and Neuron Approaches, a satisfactory level of fault-tolerance could only be obtained by retraining the degrated VLSI neurocontroller. The possibility of maintaining neurocontrol performance and stability in the presence of single weight of neuron failures was demonstrated through an automated retraining procedure of the neurocontroller based on a pre-programmed choice and sequence of the training parameters

A real time neural net estimator of fatigue life

A neural net architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the Intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopump blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines. This neural net estimator of fatigue life is seen as the enabling technology to achieve component life prognosis, and therefore would be an important part of life extending control for reusable rocket engines

Design and evaluation of a robust dynamic neurocontroller for a multivariable aircraft control problem

The design of a dynamic neurocontroller with good robustness properties is presented for a multivariable aircraft control problem. The internal dynamics of the neurocontroller are synthesized by a state estimator feedback loop. The neurocontrol is generated by a multilayer feedforward neural network which is trained through backpropagation to minimize an objective function that is a weighted sum of tracking errors, and control input commands and rates. The neurocontroller exhibits good robustness through stability margins in phase and vehicle output gains. By maintaining performance and stability in the presence of sensor failures in the error loops, the structure of the neurocontroller is also consistent with the classical approach of flight control design

A Novel Approach to Noise-Filtering Based on a Gain-Scheduling Neural Network Architecture

A gain-scheduling neural network architecture is proposed to enhance the noise-filtering efficiency of feedforward neural networks, in terms of both nominal performance and robustness. The synergistic benefits of the proposed architecture are demonstrated and discussed in the context of the noise-filtering of signals that are typically encountered in aerospace control systems. The synthesis of such a gain-scheduled neurofiltering provides the robustness of linear filtering, while preserving the nominal performance advantage of conventional nonlinear neurofiltering. Quantitative performance and robustness evaluations are provided for the signal processing of pitch rate responses to typical pilot command inputs for a modern fighter aircraft model

Continuous representation of many-fermion systems over real Slater determinants

We derive a resolution of unity over real Slater determinants using simple symmetry arguments. The resulting simplification of the measure with respect to the previous representations makes it a good candidate for stochastic evaluations

Time-dependent mean-field approximations for many-body observables

The excitation of a many-body system by a time-dependent perturbation is considered within the framework of functional integration. The stationary phase approximation to a functional-integral representation of the final expectation values of many-body observables in the interaction picture leads to a new time-dependent mean-field theory. The resulting equations of motion depend upon the observable itself and consequently are nonlocal in time. The method is illustrated by an analytically soluble application to the forced harmonic oscillator

Comment on "Monte Carlo Evaluation of Functional Integrals Using Coherent-State Slater Determinants"

In a recent letter [1], Avishai and Richert proposed a Monte Carlo evaluation of many-fermion functional integrals using real coherent-state Slater determinants

Nonlocal Phases of Local Quantum Mechanical Wavefunctions in Static and Time-Dependent Aharonov-Bohm Experiments

We show that the standard Dirac phase factor is not the only solution of the gauge transformation equations. The full form of a general gauge function (that connects systems that move in different sets of scalar and vector potentials), apart from Dirac phases also contains terms of classical fields that act nonlocally (in spacetime) on the local solutions of the time-dependent Schr\"odinger equation: the phases of wavefunctions in the Schr\"odinger picture are affected nonlocally by spatially and temporally remote magnetic and electric fields, in ways that are fully explored. These contributions go beyond the usual Aharonov-Bohm effects (magnetic or electric). (i) Application to cases of particles passing through static magnetic or electric fields leads to cancellations of Aharonov-Bohm phases at the observation point; these are linked to behaviors at the semiclassical level (to the old Werner & Brill experimental observations, or their "electric analogs" - or to recent reports of Batelaan & Tonomura) but are shown to be far more general (true not only for narrow wavepackets but also for completely delocalized quantum states). By using these cancellations, certain previously unnoticed sign-errors in the literature are corrected. (ii) Application to time-dependent situations provides a remedy for erroneous results in the literature (on improper uses of Dirac phase factors) and leads to phases that contain an Aharonov-Bohm part and a field-nonlocal part: their competition is shown to recover Relativistic Causality in earlier "paradoxes" (such as the van Kampen thought-experiment), while a more general consideration indicates that the temporal nonlocalities found here demonstrate in part a causal propagation of phases of quantum mechanical wavefunctions in the Schr\"odinger picture. This may open a direct way to address time-dependent double-slit experiments and the associated causal issuesComment: 49 pages, 1 figure, presented in Conferences "50 years of the Aharonov-Bohm effect and 25 years of the Berry's phase" (Tel Aviv and Bristol), published in Journ. Phys. A. Compared to the published paper, this version has 17 additional lines after eqn.(14) for maximum clarity, and the Abstract has been slightly modified and reduced from the published 2035 characters to the required 1920 character