Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise

Rollins Catalogue, 1981-1982

Rollins College catalogue 1981-1982 with list of faculty and students and courses by department

Advanced Materials for Exploration Task Research Results

The Advanced Materials for Exploration (AME) Activity in Marshall Space Flight Center s (MSFC s) Exploration Science and Technology Directorate coordinated activities from 2001 to 2006 to support in-space propulsion technologies for future missions. Working together, materials scientists and mission planners identified materials shortfalls that are limiting the performance of long-term missions. The goal of the AME project was to deliver improved materials in targeted areas to meet technology development milestones of NASA s exploration-dedicated activities. Materials research tasks were targeted in five areas: (1) Thermal management materials, (2) propulsion materials, (3) materials characterization, (4) vehicle health monitoring materials, and (5) structural materials. Selected tasks were scheduled for completion such that these new materials could be incorporated into customer development plans

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 178

This bibliography lists 230 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1978