[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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 chapter is devoted to a more detailed examination of game theory. Game theory is an important tool for analyzing strategic behavior, is concerned with how individuals make decisions when they recognize that their actions affect, and are affected by, the actions of other individuals or groups. Strategic behavior recognizes that the decision-making process is frequently mutually interdependent. Game theory is the study of the strategic behavior involving the interaction of two or more individuals, teams, or firms, usually referred to as players. Two game theoretic scenarios were examined in this chapter: Simultaneous-move and multi-stage games. In simultaneous-move games the players effectively move at the same time. A normal-form game summarizes the players, possible strategies and payoffs from alternative strategies in a simultaneous-move game. Simultaneous-move games may be either noncooperative or cooperative. In contrast to noncooperative games, players of cooperative games engage in collusive behavior. A Nash equilibrium, which is a solution to a problem in game theory, occurs when the players’ payoffs cannot be improved by changing strategies. Simultaneous-move games may be either one-shot or repeated games. One-shot games are played only once. Repeated games are games that are played more than once. Infinitely-repeated games are played over and over again without end. Finitely-repeated games are played a limited number of times. Finitely-repeated games have certain or uncertain ends.

An Introduction to Game Theory

This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal $\mathcal {H}_{2}$ and $\mathcal {H}_\infty $ control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

Optimal and Autonomous Control Using Reinforcement Learning: A Survey

This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and stability properties of policy iteration method for discrete-time nonlinear systems for the first time. It shows that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation. It is also proven that any of the iterative control laws can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control law, respectively, for facilitating the implementation of the iterative ADP algorithm, where the convergence of the weight matrices is analyzed. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.

http://www.derongliu.org/papers/liu-wei-TNNLS-mar-2014.pdf

Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems

In this paper, a direct adaptive interval type-2 fuzzy sliding mode control scheme is proposed for synchronizing two different chaotic systems. This method guarantees the global asymptotic stability synchronization of the two state trajectories using lyapunov stability criteria. This combination reduced the chattering phenomena of the control efforts by type-2 fuzzy paradigm. To elucidate the performance of this method, the simulation results to synchronize two different chaotic systems such as Lorenz and Chen are given. This method ensures the convergence of the synchronization error between two systems towards zero as time goes to infinity. Simulation results show the effectiveness of proposed method.

A synchronizing controller using a direct adaptive interval type-2 fuzzy sliding mode strategy

This paper proposes a reliable and efficient method for recognition in two different orientations (either left or right) by Magnetoencephalograph (MEG) signals. The brain activities are measured using different approaches with different spatial and temporal resolutions. The MEG signals are usually used for brain-computer interface (BCI) applications due to high temporal resolution. The MEG signals were recorded from different brain regions of four different human subjects during visual covert selective spatial attention task. The hybrid method proposes pre-processing; feature extraction by Hurst exponent, Morlet wavelet coefficients, and Petrosian fractal dimension; normalization; feature selection by p-value; and classification by support vector machine (SVM) and fuzzy support vector machine (FSVM). The results show that the proposed method can predict the location of the attended stimulus with a high accuracy of 91.62% and 92.28% for two different orientations with SVM and FSVM, respectively. Finally, these methods can be useful for BCI applications based on visual covert selective spatial attention.

Hybrid approach in recognition of visual covert selective spatial attention based on MEG signals

The electric power industry is confronted with a major restructuring in which operation scheduling is decided based on a competitive market. In this new arrangement, bidding strategy becomes an important issue. Participants of the deregulated energy marketplace may be able to compete better by choosing a suitable bidding strategy for trading electricity. In such a deregulated market, coalition formation with other participants may change the diffusion of profit. Therefore the problem changes to the selection of the best participant for coalition and joint bidding strategies. Different classic methods for decision making in the uncertain environment of the market can be applied to select a suitable strategy. Most of these methods, such as game theory that insures reaching the optimal solution for all market participants, require a lot of information about other market players and the market. However, in a real marketplace only a little information such as the spot price is available for all participants. A modified reinforcement-learning approach based on Q-adjusted annealing has been applied to determine the optimal strategy for a power supplier in an electricity market with multiple sources. A modified IEEE 30-bus system has been considered and the simulation results are shown to be the same as with standard game theory. The main advantage of the proposed method is that no information about other participants is required. Investigation shows that if all participants use this method they will stay in Nash equilibrium.

Q-adjusted annealing for Q-learning of bid selection in market-based multisource power systems

In this paper, the optimal adaptive leader-follower consensus of linear continuous time multi-agent systems is considered. The error dynamics of each player depends on its neighbors’ information. Detailed analysis of online optimal leader-follower consensus under known and unknown dynamics is presented. The introduced reinforcement learning-based algorithms learn online the approximate solution to algebraic Riccati equations. An optimal adaptive control technique is employed to iteratively solve the algebraic Riccati equation based on the online measured error state and input information for each agent without requiring the priori knowledge of the system matrices. The decoupling of the multi-agent system global error dynamics facilitates the employment of policy iteration and optimal adaptive control techniques to solve the leaderfollower consensus problem under known and unknown dynamics. Simulation results verify the effectiveness of the proposed methods.

/pdf/optimal-adaptive-leader-follower-consensus-of-linear-multi-4ugkrle5tt.pdf

Optimal adaptive leader-follower consensus of linear multi-agent systems: Known and unknown dynamics

This paper presents a control algorithm for a type I diabetes mellitus patient under an intensive insulin treatment The control algorithm employs a robust Hinfin controller to regulate the blood glucose level The control scheme is based on closed-loop feedback strategy to overcome the variability in the glucose-insulin dynamics from patient to patient Controller performance is assessed in terms of its ability to track a normoglycemic set point (70 mg/dl) in response to multiple meal disturbances resulting from food intake Simulations results show that resulted controller are robust to 90% parameter variations from mean value on nonlinear patient model The proposed approach can successfully regulate the blood glucose level and represents more effective results in terms of robustness to uncertainty, in comparison with other existing algorithms

Mohammad-Bagher Naghibi-Sistani

Papers

A synchronizing controller using a direct adaptive interval type-2 fuzzy sliding mode strategy

Hybrid approach in recognition of visual covert selective spatial attention based on MEG signals

Q-adjusted annealing for Q-learning of bid selection in market-based multisource power systems

Optimal adaptive leader-follower consensus of linear multi-agent systems: Known and unknown dynamics

An Automatic Insulin Infusion System based on H-Infinity Control Technique