Evolution of neurocontrollers in changing environments

One of the most challenging aspects of the control theory is the design and implementation of controllers that can deal with changing environments, i. e., non stationary systems. Quite good progress has been made on this area by using different kind of models: neural networks, fuzzy systems, evolutionary algorithms, etc. Our approach consists in the use of a memory based evolutionary algorithm, specially designed in such a way that can be used to evolve neurocontrollers to be applied in changing environments. In this paper, we describe our architecture, and present an example of its application to a typical control problem.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Prior knowledge in evolutionary fuzzy recurrent controllers design

A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. As it is well known, the use of prior knowledge can dramatically improve the performance and quality of the fuzzy system design process. In previous works we have introduced the RFV model, a representation for recurrent fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, ful lling the completeness property and providing a simple way to introduce prior knowledge. In this work we present our current approach in the study of the inclusion of prior knowledge in the context of the RFV model.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Evolution of recurrent fuzzy controllers

The main advantage of a recurrent architecture is the ability to store information from prior system states. A recurrent fuzzy controller includes hidden fuzzy variables which makes the controller more appropriate to deal with dynamic systems. We are currently investigating the effect of evolution of recurrent fuzzy controllers by applying the FV representation, which provides a set of advantages that can signi catively benefit the quality of the knowledge insertion process.Eje: Sistemas de información y MetaheurísticaRed de Universidades con Carreras en Informática (RedUNCI

An architecture for fuzzy logic controllers evolution and learning in microcontrollers based environments

The growing number of control models based on combinations of neural networks, fuzzy systems and evolutionary algorithms shows that they represent a flexible and powerful approach. However, most of these models assume that there is enough CPU power for the evolutionary and learning algorithms, which in a large number of cases is an unrealistic assumption. It is usual that the control tasks are performed by small microcontrollers, which are very near to or embedded in the plant, with low power, low cost and dedicated to a single task. This work proposes an architecture for evolution and learning in adaptive control, specifically designed to operate in microcontrollers based environments. An evaluation on a simulated temperature control environment is provided, together with details on the current hardware implementation.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Evolution of Voronoi based Fuzzy Recurrent Controllers

A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. Among the most successful methods to automate the fuzzy controllers development process are evolutionary algorithms. In this work, we propose the Recurrent Fuzzy Voronoi (RFV) model, a representation for recurrent fuzzy systems. It is an extension of the FV model proposed by Kavka and Schoenauer that extends the application domain to include temporal problems. The FV model is a representation for fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, fulfilling the $\epsilon$-completeness property and providing a simple way to introduce a priory knowledge. In the proposed representation, the temporal relations are embedded by including internal units that provide feedback by connecting outputs to inputs. These internal units act as memory elements. In the RFV model, the semantic of the internal units can be specified together with the a priori rules. The geometric interpretation of the rules allows the use of geometric variational operators during the evolution. The representation and the algorithms are validated in two problems in the area of system identification and evolutionary robotics

Evolution of neurocontrollers in changing environments

One of the most challenging aspects of the control theory is the design and implementation of controllers that can deal with changing environments, i. e., non stationary systems. Quite good progress has been made on this area by using different kind of models: neural networks, fuzzy systems, evolutionary algorithms, etc. Our approach consists in the use of a memory based evolutionary algorithm, specially designed in such a way that can be used to evolve neurocontrollers to be applied in changing environments. In this paper, we describe our architecture, and present an example of its application to a typical control problem.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Evolución de controladores difusos recurrentes basados en diagramas de voronoi

Para muchos procesos del mundo real es posible dise ñar un controlador difuso que provea buena regularidad usando sólo conocimiento experto. No obstante ello, para lograr un desempeñno satisfactorio es necesario hacer uso de métodos más so fisticados. En este trabajo proponemos un modelo basado en sistemas difusos recurrentes, donde el antecedente de las reglas está determinado por una función de pertenencia multidimensional de finida en términos de regiones de Voronoi. Las conexiones recurrentes permiten mantener una memoria que guarda información previa. Un algoritmo evolutivo puede evolucionar todas las componentes del sistema difuso recurrente. Además es posible insertar en el sistema conocimiento a priori de forma simple y efectiva. El modelo propuesto es evaluado sobre un problema de control de un robot móvil que debe avanzar evitando obstáculos y siguiendo una trayectoria dirigida por se ñales luminosas.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Evolución de controladores difusos recurrentes basados en diagramas de voronoi

Para muchos procesos del mundo real es posible dise ñar un controlador difuso que provea buena regularidad usando sólo conocimiento experto. No obstante ello, para lograr un desempeñno satisfactorio es necesario hacer uso de métodos más so fisticados. En este trabajo proponemos un modelo basado en sistemas difusos recurrentes, donde el antecedente de las reglas está determinado por una función de pertenencia multidimensional de finida en términos de regiones de Voronoi. Las conexiones recurrentes permiten mantener una memoria que guarda información previa. Un algoritmo evolutivo puede evolucionar todas las componentes del sistema difuso recurrente. Además es posible insertar en el sistema conocimiento a priori de forma simple y efectiva. El modelo propuesto es evaluado sobre un problema de control de un robot móvil que debe avanzar evitando obstáculos y siguiendo una trayectoria dirigida por se ñales luminosas.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Aplicaciones de redes neuronales a problemas te´oricos y de control

Esta presentación contiene un resumen de los trabajos más importantes que se están desarrollando actualmente en la Línea de Investigación en Redes Neuronales en el ámbito del LIDIC, el Laboratorio de Investigación en Inteligencia Computacional de la Universidad Nacional de San Luis. Los tres temas principales son control en ambientes cambiantes, control neuro difuso y aplicación de redes neuronales a problemas teóricos. Las secciones siguientes contienen una descripción de cada uno de ellos.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Evolution of recurrent fuzzy controllers

The main advantage of a recurrent architecture is the ability to store information from prior system states. A recurrent fuzzy controller includes hidden fuzzy variables which makes the controller more appropriate to deal with dynamic systems. We are currently investigating the effect of evolution of recurrent fuzzy controllers by applying the FV representation, which provides a set of advantages that can signi catively benefit the quality of the knowledge insertion process.Eje: Sistemas de información y MetaheurísticaRed de Universidades con Carreras en Informática (RedUNCI