The posterity of Zadeh's 50-year-old paper: A retrospective in 101 Easy Pieces – and a Few More

International audienceThis article was commissioned by the 22nd IEEE International Conference of Fuzzy Systems (FUZZ-IEEE) to celebrate the 50th Anniversary of Lotfi Zadeh's seminal 1965 paper on fuzzy sets. In addition to Lotfi's original paper, this note itemizes 100 citations of books and papers deemed “important (significant, seminal, etc.)” by 20 of the 21 living IEEE CIS Fuzzy Systems pioneers. Each of the 20 contributors supplied 5 citations, and Lotfi's paper makes the overall list a tidy 101, as in “Fuzzy Sets 101”. This note is not a survey in any real sense of the word, but the contributors did offer short remarks to indicate the reason for inclusion (e.g., historical, topical, seminal, etc.) of each citation. Citation statistics are easy to find and notoriously erroneous, so we refrain from reporting them - almost. The exception is that according to Google scholar on April 9, 2015, Lotfi's 1965 paper has been cited 55,479 times

Methods for designing and optimizing fuzzy controllers

We start by discussing fuzzy sets and the algebra of fuzzy sets. We consider some properties of fuzzy modeling tools. This is followed by considering the Mamdani and Sugeno models for designing fuzzy controllers. Various methods for using sets of data for desining controllers are discussed. This is followed by a chapter illustrating the use of genetic algorithms in designing and optimizing fuzzy controllers.Finally we look at some previous applications of fuzzy control in telecommunication networks, and illustrate a simple application that was developed as part of the present work

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Online rescheduling for mass rapid transit system using evolutionary techniques with fuzzy aggregation of multiple objectives

Master'sMASTER OF ENGINEERIN

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

Intelligent Control Architecture For Motion Learning in Robotics Applications

Abstract: The investigation of this Thesis was focused on how motion abilities can be learned by a robot. The main goal was to design and test a control architecture capable of learning how to properly move different simulated robots, through the use of Arti�cial Intelligence (AI) methods. With this purpose, a simulation environment and a set of simulated robots were created in order to test the control architecture. The robots were constructed with a simple geometry using links and joints. A fuzzy controller was designed to control the motors position. The control architecture design was based on subsumption and some AI methods that allowed the simulated robot to find and learn a set of motions based on targets. These methods were a genetic algorithm (GA) and a set of artificial neural networks (ANN). The GA was used to find the adequate robot movements for an specific target, while the ANNs were used to learn and perform such movements eficiently. The advantage of this approach was that, no knowledge of the environment or robot model is needed. The robot learns how to move its own body in order to achieve a determined task. In addition, the learned motions can be used to achieve complex movement execution in a further research. A set of experiments were performed in the simulator in order to show the performance of the control architecture in every one of its stages. The results showed that the proposed architecture was able to learn and perform basic movements of a robot independently of the environment or the robot defined structure.En esta Tesis, se investiga cómo las habilidades de movimiento en un robot, pueden ser aprendidas de forma automática. El objetivo principal fue dise~nar y probar una arquitectura de control capaz de aprender a mover adecuadamente diferentes robots simulados, mediante el uso de métodos de Inteligencia Artificial (IA). Con este propósito, se dise~no un entorno de simulación y un conjunto de robots simulados con el fin de probar la arquitectura de control. Los robots fueron construidos con una geometría muy simple utilizando enlaces y uniones (actuadores), y un controlador difuso fue dise~nado para controlar la posición de los actuadores. El dise~no de la arquitectura de control se basa en el concepto de subsunción (subsumption) y algunos métodos de IA que permiten al robot simulado determinar y aprender una serie de movimientos basados en objetivos. Los métodos usados son un algoritmo genético (GA) y un conjunto de redes neuronales artificiales (ANN). El GA se utiliza para encontrar los movimientos adecuados que el robot debe realizar para alcanzar un objetico específico, mientras que las redes neuronales se utilizan para aprender y realizar estos movimientos de forma eficiente. La ventaja de este enfoque es que, no es necesario conocer el entorno o tener un modelo del robot, sino que el robot aprende cómo mover su propio cuerpo en un ambiente definido con el fin de lograr una tarea determinada. Además, en una posterior investigación, es posible utilizar los movimientos aprendidos para realizar movimientos o tareas más complejas con los robots. Un conjunto de experimentos se llevaron a cabo en el simulador para mostrar el desempe~no de la arquitectura de control en cada una de sus etapas. Los resultados muestran que la arquitectura propuesta es capaz de aprender y realizar los movimientos del robot independientemente del medio ambiente o la estructura definida del robot.Maestrí

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms