https://axon.cs.byu.edu/~martinez/classes/678/Presentations/Yao.pdf

Extreme learning machine: Theory and applications

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

/pdf/reinforcement-learning-a-survey-38lmyylg2y.pdf

Reinforcement learning: a survey

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

Reinforcement Learning: A Survey

Flight performance of a small, low-altitude rocket.

Flying qualities of aircraft with strong lateral-directional coupling may be unsatisfactory without command and stability augmentation. Although sensitivity to stability derivative variation may be decreased by such a flight control system (FCS), deficiencies associated with control derivative uncertainty may persist or worsen. This is especially true in systems with necessarily high gains, e.g., the Space Shuttle Entry FCS. Effects of stability/control derivative variations on the flying qualities of the Space Shuttle (with and without FCS) have been investigated, and the unaugmented Space Shuttle has been simulated in flight using Princeton's Variable-Response Research Aircraft (VRA).

Flying qualities of an aircraft with strong lateral-directional coupling

Introduction R EFERENCE 1 discusses effects of speed-dependent variations in thrust on the long-period motions of aircraft in supersonic flight. Its principal contribution is the demonstration of effects that a complex pair of zeros have on speed-tothrottle feedback. It is concluded that there are fundamental differences between thrust/speed effects in subsonic and supersonic flight. It also is stated that "thrust variations with speed (or Mach number) have practically no effect on the phugoid in supersonic flight," then that "the phugoid is not influenced at all in supersonic flight." The purpose of this Comment is to show that thrust/speed effects are indeed small but not zero in supersonic flight and that this is a kinematic effect of increasing speed rather than an aerodynamic effect of Mach number. In the process, a simplified analytical model is offered for further study. Furthermore, it is noted that pitching moment/speed sensitivity due to thrust-axis offset is likely to have important long-period effect even when the direct thrust/speed effects are small.

Comment on 'Effect of thrust/speed dependence on long-period dynamics in supersonic flight'

Passenger vehicles fueled by hydrocarbons or alcohols and powered by proton exchange membrane (PEM) fuel cells address world air quality and fuel supply concerns while avoiding hydrogen infrastructure and on-board storage problems. Reduction of the carbon monoxide concentration in the on-board fuel processor's hydrogen-rich gas by the preferential oxidizer (PrOx) under dynamic conditions is crucial to avoid poisoning of the PEM fuel cell's anode catalyst and thus malfunction of the fuel cell vehicle. A dynamic control scheme is proposed for a single-stage, tubular, cooled PrOx that performs better than, but retains the reliability and ease of use of conventional industrial controllers. The proposed hybrid control system contains a CMAC artificial neural network in parallel with a conventional PID controller. By using a computer simulation, it was found that the proposed hybrid controller generalizes well to novel driving sequences after being trained on other driving sequences with similar or slower transients. Although it is similar to the PID in terms of software requirements and design effort, the hybrid controller performs significantly better than the PID in terms of H/sub 2/ conversion setpoint regulation and PrOx outlet CO reduction.

The application of neural networks to fuel processors for fuel cell vehicles

Space shuttle transition trajectory optimization for cruising flight entry, considering longitudinal control, pitchup instability and angle of attack

Robert F. Stengel

Papers

Flight performance of a small, low-altitude rocket.

Flying qualities of an aircraft with strong lateral-directional coupling

Comment on 'Effect of thrust/speed dependence on long-period dynamics in supersonic flight'

The application of neural networks to fuel processors for fuel cell vehicles

Space shuttle transition to cruising flight