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

A method of designing robust controllers for nonlinear systems with uncertain parameters is presented. The method produces a controller with feedback linearization structure that minimizes a weighted combination of the probabilities of instability and having unsatisfactory performance. The method is demonstrated for a simple nonlinear system consisting of two masses connected by a linear-cubic spring.

/pdf/robust-control-of-nonlinear-systems-with-parametric-3mv7fsb5it.pdf

Robust control of nonlinear systems with parametric uncertainty

Altitude stability in supersonic cruising flight

Paper: Digital flight control design for a tandem-rotor helicopter

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 cerebellar model articulation controller artificial neural network in parallel with a conventional proportional-integral-derivative (PID) controller. A computer simulation of the preferential oxidation reactor was used to assess the abilities of the proposed controller and compare its performance to the performance of conventional controllers. Realistic input patterns were generated for the PrOx by using models of vehicle power demand and upstream fuel-processor components to convert the speed sequences in the Federal Urban Driving Schedule to PrOx inlet temperatures, concentrations, and flow rates. 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 hydrogen conversion setpoint regulation and PrOx outlet carbon monoxide reduction.

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

Real-time, rule-based guidance systems for autonomous vehicles on limited-access highways are investigated. The goal of these systems is to plan trajectories that are safe while satisfying the driver's requests based on stochastic information about the vehicle state and the surrounding traffic. A rule-based system is used for high-level planning. Given a stochastic model of the traffic situation driven by current measurements, the probable evolution of traffic and the best trajectory to follow are predicted. Simulation results assess the impact of uncertain knowledge of traffic on the performance of the guidance system, showing that uncertainty can and must be taken into account.

/pdf/probability-based-decision-making-for-automated-highway-1ll2dbrl1i.pdf

Robert F. Stengel

Papers

Robust control of nonlinear systems with parametric uncertainty

Altitude stability in supersonic cruising flight

Paper: Digital flight control design for a tandem-rotor helicopter

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

Probability-based decision making for automated highway driving