Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad

A general learning co-evolution method to generalize autonomous robot navigation behavior

Congress on Evolutionary Computation. La Jolla, CA, 16-19 July 2000.A new coevolutive method, called Uniform Coevolution, is introduced, to learn weights for a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collision avoidance. The coevolutive method allows the evolution of the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with or without coevolution have been tested in a set of environments and the capability for generalization has been shown for each learned behavior. A simulator based on the mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to example-based problems

Neural networks in geophysical applications

Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size and architecture

Load forecasting on the user‐side by means of computational intelligence algorithms

Nowadays, it would be very difficult to deny the need to prioritize sustainable development through energy efficiency at all consumption levels. In this context, an energy management system (EMS) is a suitable option for continuously improving energy efficiency, particularly on the user side. An EMS is a set of technological tools that manages energy consumption information and allows its analysis. EMS, in combination with information technologies, has given rise to intelligent EMS (iEMS), which, aside from lending support to monitoring and reporting functions as an EMS does, it has the ability to model, forecast, control and diagnose energy consumption in a predictive way. The main objective of an iEMS is to continuously improve energy efficiency (on-line) as automatically as possible. The core of an iEMS is its load modeling forecasting system (LMFS). It takes advantage of historical information on energy consumption and energy-related variables in order to model and forecast load profiles and, if available, generator profiles. These models and forecasts are the main information used for iEMS applications for control and diagnosis. That is why in this thesis we have focused on the study, analysis and development of LMFS on the user side. The fact that the LMFS is applied on the user side to support an iEMS means that specific characteristics are required that in other areas of load forecasting they are not. First of all, the user-side load profiles (LPs) have a higher random behavior than others, as for example, in power system distribution or generation. This makes the modeling and forecasting process more difficult. Second, on the user side --for example an industrial user-- there is a high number and variety of places that can be monitored, modeled and forecasted, as well as their precedence or nature. Thus, on the one hand, an LMFS requires a high degree of autonomy to automatically or autonomously generate the demanded models. And on the other hand, it needs a high level of adaptability in order to be able to model and forecast different types of loads and different types of energies. Therefore, the addressed LMFS are those that do not look only for accuracy, but also adaptability and autonomy. Seeking to achieve these objectives, in this thesis work we have proposed three novel LMFS schemes based on hybrid algorithms from computational intelligence, signal processing and statistical theory. The first of them looked to improve adaptability, keeping in mind the importance of accuracy and autonomy. It was called an evolutionary training algorithm (ETA) and is based on adaptivenetwork-based-fuzzy-inference system (ANFIS) that is trained by a multi-objective genetic algorithm instead of its traditional training algorithm. As a result of this hybrid, the generalization capacity was improved (avoiding overfitting) and an easily adaptable training algorithm for new adaptive networks based on traditional ANFIS was obtained. The second scheme deals with LMF autonomy in order to build models from multiple loads automatically. Similar to the previous proposal, an ANFIS and a MOGA were used. In this case, the MOGA was used to find a near-optimal configuration for the ANFIS instead of training it. The LMFS relies on this configuration to work properly, as well as to maintain accuracy and generalization capabilities. Real data from an industrial scenario were used to test the proposed scheme and the multi-site modeling and self-configuration results were satisfactory. Furthermore, other algorithms were satisfactorily designed and tested for processing raw data in outlier detection and gap padding. The last of the proposed approaches sought to improve accuracy while keeping autonomy and adaptability. It took advantage of dominant patterns (DPs) that have lower time resolution than the target LP, so they are easier to model and forecast. The Hilbert-Huang transform and Hilbert-spectral analysis were used for detecting and selecting the DPs. Those selected were used in a proposed scheme of partial models (PM) based on parallel ANFIS or artificial neural networks (ANN) to extract the information and give it to the main PM. Therefore, LMFS accuracy improved and the user-side LP noising problem was reduced. Additionally, in order to compensate for the added complexity, versions of self-configured sub-LMFS for each PM were used. This point was fundamental since, the better the configuration, the better the accuracy of the model; and subsequently the information provided to the main partial model was that much better. Finally, and to close this thesis, an outlook of trends regarding iEMS and an outline of several hybrid algorithms that are pending study and testing are presented.En el contexto energético actual y particularmente en el lado del usuario, el concepto de sistema de gestión energética (EMS) se presenta como una alternativa apropiada para mejorar continuamente la eficiencia energética. Los EMSs en combinación con las tecnologías informáticas dan origen al concepto de iEMS, que además de soportar las funciones de los EMS, tienen la capacidad de modelar, pronosticar, controlar y supervisar los consumos energéticos. Su principal objetivo es el de realizar una mejora continua, lo más autónoma posible y predictiva de la eficiencia energética. Este tipo de sistemas tienen como núcleo fundamental el sistema de modelado y pronóstico de consumos (Load Modeling and Forecasting System, LMFS). El LMFS está habilitado para pronosticar el comportamiento futuro de cargas y, si es necesario, de generadores. Es sobre estos pronósticos sobre los cuales el iEMS puede realizar sus tareas automáticas y predictivas de optimización y supervisión. Los LMFS en el lado del usuario son el foco de esta tesis. Un LMFS en el lado del usuario, diseñado para soportar un iEMS requiere o demanda ciertas características que en otros contextos no serían tan necesarias. En primera estancia, los perfiles de los usuarios tienen un alto grado de aleatoriedad que los hace más difíciles de pronosticar. Segundo, en el lado del usuario, por ejemplo en la industria, el gran número de puntos a modelar requiere que el LMFS tenga por un lado, un nivel elevado de autonomía para generar de la manera más desatendida posible los modelos. Por otro lado, necesita un nivel elevado de adaptabilidad para que, usando la misma estructura o metodología, pueda modelar diferentes tipos de cargas cuya procedencia pude variar significativamente. Por lo tanto, los sistemas de modelado abordados en esta tesis son aquellos que no solo buscan mejorar la precisión, sino también la adaptabilidad y autonomía. En busca de estos objetivos y soportados principalmente por algoritmos de inteligencia computacional, procesamiento de señales y estadística, hemos propuesto tres algoritmos novedosos para el desarrollo de un LMFS en el lado del usuario. El primero de ellos busca mejorar la adaptabilidad del LMFS manteniendo una buena precisión y capacidad de autonomía. Denominado ETA, consiste del uso de una estructura ANFIS que es entrenada por un algoritmo genético multi objetivo (MOGA). Como resultado de este híbrido, obtenemos un algoritmo con excelentes capacidades de generalización y fácil de adaptar para el entrenamiento y evaluación de nuevas estructuras adaptativas basadas en ANFIS. El segundo de los algoritmos desarrollados aborda la autonomía del LMFS para así poder generar modelos de múltiples cargas. Al igual que en la anterior propuesta usamos un ANFIS y un MOGA, pero esta vez el MOGA en vez de entrenar el ANFIS, se utiliza para encontrar la configuración cuasi-óptima del ANFIS. Encontrar la configuración apropiada de un ANFIS es muy importante para obtener un buen funcionamiento del LMFS en lo que a precisión y generalización respecta. El LMFS propuesto, además de configurar automáticamente el ANFIS, incluyó diversos algoritmos para procesar los datos puros que casi siempre estuvieron contaminados de datos espurios y gaps de información, operando satisfactoriamente en las condiciones de prueba en un escenario real. El tercero y último de los algoritmos buscó mejorar la precisión manteniendo la autonomía y adaptabilidad, aprovechando para ello la existencia de patrones dominantes de más baja resolución temporal que el consumo objetivo, y que son más fáciles de modelar y pronosticar. La metodología desarrollada se basa en la transformada de Hilbert-Huang para detectar y seleccionar tales patrones dominantes. Además, esta metodología define el uso de modelos parciales de los patrones dominantes seleccionados, para mejorar la precisión del LMFS y mitigar el problema de aleatoriedad que afecta a los consumos en el lado del usuario. Adicionalmente, se incorporó el algoritmo de auto configuración que se presentó en la propuesta anterior para hallar la configuración cuasi-óptima de los modelos parciales. Este punto fue crucial puesto que a mejor configuración de los modelos parciales mayor es la mejora en precisión del pronóstico final. Finalmente y para cerrar este trabajo de tesis, se realizó una prospección de las tendencias en cuanto al uso de iEMS y se esbozaron varias propuestas de algoritmos híbridos, cuyo estudio y comprobación se plantea en futuros estudios

Autonomous Robots and Behavior Initiators

We use an autonomous neural controller (ANC) that handles the mechanical behavior of virtual, multi-joint robots, with many moving parts and sensors distributed through the robot’s body, satisfying basic Newtonian laws. As in living creatures, activities inside the robot include behavior initiators: self-activating networks that burn energy and function without external stimulus. Autonomy is achieved by mimicking the dynamics of biological brains, in resting situations, a default state network (DSN), specialized set of energy burning neurons, assumes control and keeps the robot in a safe condition, where other behaviors can be brought to use. Our ANC contains several kinds of neural nets trained with gradient descent to perform specialized jobs. The first group generates moving wave activities in the robot muscles, the second yields basic position/presence prediction information about sensors, the third acts as timing masters, empowering sequential tasks. We add a fourth category of self-activating networks that push behavior from the inside. Through evolutive methods, the composed network share clue information along a few connecting weights, producing self-motivated robots, capable of achieving noticeable self-level of competence. We show that this spirited robot interacts with humans and, through appropriate interfaces, learn complex behaviors that satisfy unknown, subjacent human expectative

Generalization capabilities of co-evolution in learning robot behavior

In this article, a co-evolutive method is used to evolve neural controllers for general obstacle-avoidance of a Braitenberg vehicle. During a first evolutionary process, Evolution Strategies were applied to generate neural controllers; the generality of the obtained behaviors was quite poor. During a second evolutionary process, a new co-evolutive method, called Uniform Co-evolution, is introduced to co-evolve both the controllers and the environment. A comparison of both methods shows that the co-evolutive approach improves the generality of controllers.Publicad