571 research outputs found
Intelligent flight control systems
The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Advances in Theoretical and Computational Energy Optimization Processes
The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes
Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit
The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed. An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced. Design of robust knowledge bases is performed using a developed computational intelligence – quantum / soft computing toolkit (QC/SCOptKBTM). The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described. The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described. The general design methodology of a generalizing control unit based on the physical laws of quantum computing (quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal) is considered. The modernization of the pattern recognition system based on stereo vision technology presented. The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system
A Novel Self-Organizing PID Approach for Controlling Mobile Robot Locomotion
A novel self-organizing fuzzy proportional-integral-derivative (SOF-PID) control system is proposed in this paper. The proposed system consists of a pair of control and reference models, both of which are implemented by a first-order autonomous learning multiple model (ALMMo) neuro-fuzzy system. The SOF-PID controller self-organizes and self-updates the structures and meta-parameters of both the control and reference models during the control process "on the fly". This gives the SOF-PID control system the capability of quickly adapting to entirely new operating environments without a full re-training. Moreover, the SOF-PID control system is free from user- and problem-specific parameters and is entirely data-driven. Simulations and real-world experiments with mobile robots demonstrate the effectiveness and validity of the proposed SOF-PID control system
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Humanoid Robots
For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion
Understanding Dynamic Systems and Non-linearity
This empirical dissertation deals with how, and how well, people understand dynamic systems and non-linear processes
Simulation of Human and Artificial Emotion (SHArE)
The framework for Simulation of Human and Artificial Emotion (SHArE)
describes the architecture of emotion in terms of parameters transferable
between psychology, neuroscience, and artificial intelligence. These parameters
can be defined as abstract concepts or granularized down to the voltage levels
of individual neurons. This model enables emotional trajectory design for
humans which may lead to novel therapeutic solutions for various mental health
concerns. For artificial intelligence, this work provides a compact notation
which can be applied to neural networks as a means to observe the emotions and
motivations of machines
Models for time series prediction based on neural networks. Case study : GLP sales prediction from ANCAP.
A time series is a sequence of real values that can be considered as observations of a certain
system. In this work, we are interested in time series coming from dynamical systems. Such
systems can be sometimes described by a set of equations that model the underlying mechanism
from where the samples come. However, in several real systems, those equations are
unknown, and the only information available is a set of temporal measures, that constitute
a time series. On the other hand, by practical reasons it is usually required to have a prediction,
v.g. to know the (approximated) value of the series in a future instant t. The goal of
this thesis is to solve one of such real-world prediction problem: given historical data related
with the lique ed bottled propane gas sales, predict the future gas sales, as accurately as
possible. This time series prediction problem is addressed by means of neural networks,
using both (dynamic) reconstruction and prediction. The problem of to dynamically reconstruct
the original system consists in building a model that captures certain characteristics
of it in order to have a correspondence between the long-term behavior of the model and of
the system.
The networks design process is basically guided by three ingredients. The dimensionality
of the problem is explored by our rst ingredient, the Takens-Mañé's theorem. By means
of this theorem, the optimal dimension of the (neural) network input can be investigated.
Our second ingredient is a strong theorem: neural networks with a single hidden layer are
universal approximators. As the third ingredient, we faced the search of the optimal size
of the hidden layer by means of genetic algorithms, used to suggest the number of hidden
neurons that maximizes a target tness function (related with prediction errors). These
algorithms are also used to nd the most in uential networks inputs in some cases. The
determination of the hidden layer size is a central (and hard) problem in the determination
of the network topology.
This thesis includes a state of the art of neural networks design for time series prediction, including
related topics such as dynamical systems, universal approximators, gradient-descent
searches and variations, as well as meta-heuristics. The survey of the related literature is
intended to be extensive, for both printed material and electronic format, in order to have a
landscape of the main aspects for the state of the art in time series prediction using neural
networks. The material found was sometimes extremely redundant (as in the case of the
back-propagation algorithm and its improvements) and scarce in others (memory structures
or estimation of the signal subspace dimension in the stochastic case). The surveyed literature
includes classical research works ([27], [50], [52]) as well as more recent ones ([79] , [16]
or [82]), which pretends to be another contribution of this thesis.
Special attention is given to the available software tools for neural networks design and time
series processing. After a review of the available software packages, the most promising
computational tools for both approaches are discussed. As a result, a whole framework
based on mature software tools was set and used. In order to work with such dynamical
systems, software intended speci cally for the analysis and processing of time series was
employed, and then chaotic series were part of our focus.
Since not all randomness is attributable to chaos, in order to characterize the dynamical
system generating the time series, an exploration of chaotic-stochastic systems is required,
as well as network models to predict a time series associated to one of them. Here we
pretend to show how the knowledge of the domain, something extensively treated in the
bibliography, can be someway sophisticated (such as the Lyapunov's spectrum for a series
or the embedding dimension). In order to model the dynamical system generated by the time series we used the state-space model, so the time series prediction was translated in the prediction of the next system
state. This state-space model, together with the delays method (delayed coordinates) have
practical importance for the development of this work, speci cally, the design of the input
layer in some networks (multi-layer perceptrons - MLPs) and other parameters (taps in the
TFLNs). Additionally, the rest of the network components where determined in many cases
through procedures traditionally used in neural networks : genetic algorithms.
The criteria of model (network) selection are discussed and a trade-o between performance
and network complexity is further explored, inspired in the Rissanen's minimum description
length and its estimation given by the chosen software. Regarding the employed network
models, the network topologies suggested from the literature as adequate for the prediction
are used (TLFNs and recurrent networks) together with MLPs (a classic of arti cial neural
networks) and networks committees. The e ectiveness of each method is con rmed for the
proposed prediction problem. Network committees, where the predictions are a naive convex
combination of predictions from individual networks, are also extensively used.
The need of criteria to compare the behaviors of the model and of the real system, in the long
run, for a dynamic stochastic systems, is presented and two alternatives are commented.
The obtained results proof the existence of a solution to the problem of learning of the
dependence Input ! Output . We also conjecture that the system is dynamic-stochastic
but not chaotic, because we only have a realization of the random process corresponding to
the sales. As a non-chaotic system, the mean of the predictions of the sales would improve
as the available data increase, although the probability of a prediction with a big error is
always non-null due to the randomness present. This solution is found in a constructive and
exhaustive way. The exhaustiveness can be deduced from the next ve statements:
the design of a neural network requires knowing the input and output dimension,the
number of the hidden layers and of the neurons in each of them.
the use of the Takens-Mañé's theorem allows to derive the dimension of the input data
by theorems such as the Kolmogorov's and Cybenko's ones the use of multi-layer
perceptrons with only one hidden layer is justi ed so several of such models were
tested
the number of neurons in the hidden layer is determined many times heuristically
using genetic algorithms
a neuron in the output gives the desired prediction
As we said, two tasks are carried out: the development of a time series prediction model
and the analysis of a feasible model for the dynamic reconstruction of the system. With
the best predictive model, obtained by an ensemble of two networks, an acceptable average
error was obtained when the week to be predicted is not adjacent to the training set (7.04%
for the week 46/2011). We believe that these results are acceptable provided the quantity
of information available, and represent an additional validation that neural networks are
useful for time series prediction coming from dynamical systems, no matter whether they
are stochastic or not.
Finally, the results con rmed several already known facts (such as that adding noise to the
inputs and outputs of the training values can improve the results; that recurrent networks
trained with the back-propagation algorithm don't have the problem of vanishing gradients
in short periods and that the use of committees - which can be seen as a very basic of
distributed arti cial intelligence - allows to improve signi cantly the predictions).Una serie temporal es una secuencia de valores reales que pueden ser considerados como observaciones
de un cierto sistema. En este trabajo, estamos interesados en series temporales
provenientes de sistemas dinámicos. Tales sistemas pueden ser algunas veces descriptos por
un conjunto de ecuaciones que modelan el mecanismo subyacente que genera las muestras.
sin embargo, en muchos sistemas reales, esas ecuaciones son desconocidas, y la única información disponible es un conjunto de medidas en el tiempo, que constituyen la serie temporal.
Por otra parte, por razones prácticas es generalmente requerida una predicción, es decir,
conocer el valor (aproximado) de la serie en un instante futuro t. La meta de esta tesis es
resolver un problema de predicción del mundo real: dados los datos históricos relacionados
con las ventas de gas propano licuado, predecir las ventas futuras, tan aproximadamente
como sea posible. Este problema de predicción de series temporales es abordado por medio
de redes neuronales, tanto para la reconstrucción como para la predicción. El problema de
reconstruir dinámicamente el sistema original consiste en construir un modelo que capture
ciertas caracterÃsticas de él de forma de tener una correspondencia entre el comportamiento
a largo plazo del modelo y del sistema.
El proceso de diseño de las redes es guiado básicamente por tres ingredientes. La dimensionalidad
del problema es explorada por nuestro primer ingrediente, el teorema de Takens-Mañé.
Por medio de este teorema, la dimensión óptima de la entrada de la red neuronal puede ser
investigada. Nuestro segundo ingrediente es un teorema muy fuerte: las redes neuronales
con una sola capa oculta son un aproximador universal. Como tercer ingrediente, encaramos
la búsqueda del tamaño oculta de la capa oculta por medio de algoritmos genéticos, usados
para sugerir el número de neuronas ocultas que maximizan una función objetivo (relacionada
con los errores de predicción). Estos algoritmos se usan además para encontrar las entradas
a la red que influyen más en la salida en algunos casos. La determinación del tamaño de la
capa oculta es un problema central (y duro) en la determinación de la topologÃa de la red.
Esta tesis incluye un estado del arte del diseño de redes neuronales para la predicción de series
temporales, incluyendo tópicos relacionados tales como sistemas dinámicos, aproximadores
universales, búsquedas basadas en el gradiente y sus variaciones, asà como meta-heurÃsticas.
El relevamiento de la literatura relacionada busca ser extenso, para tanto el material impreso
como para el que esta en formato electrónico, de forma de tener un panorama de los
principales aspectos del estado del arte en la predicción de series temporales usando redes
neuronales. El material hallado fue algunas veces extremadamente redundante (como en
el caso del algoritmo de retropropagación y sus mejoras) y escaso en otros (estructuras de
memoria o estimación de la dimensión del sub-espacio de señal en el caso estocástico). La
literatura consultada incluye trabajos de investigación clásicos ( ([27], [50], [52])' asà como
de los más reciente ([79] , [16] or [82]).
Se presta especial atención a las herramientas de software disponibles para el diseño de redes
neuronales y el procesamiento de series temporales. Luego de una revisión de los paquetes
de software disponibles, las herramientas más promisiorias para ambas tareas son discutidas.
Como resultado, un entorno de trabajo completo basado en herramientas de software maduras fue definido y usado. Para trabajar con los mencionados sistemas dinámicos, software
especializado en el análisis y proceso de las series temporales fue empleado, y entonces
las series caóticas fueron estudiadas.
Ya que no toda la aleatoriedad es atribuible al caos, para caracterizar al sistema dinámico
que genera la serie temporal se requiere una exploración de los sistemas caóticos-estocásticos,
asà como de los modelos de red para predecir una serie temporal asociada a uno de ellos.
Aquà se pretende mostrar cómo el conocimiento del dominio, algo extensamente tratado en
la literatura, puede ser de alguna manera sofisticado (tal como el espectro de Lyapunov de
la serie o la dimensión del sub-espacio de señal).
Para modelar el sistema dinámico generado por la serie temporal se usa el modelo de espacio
de estados, por lo que la predicción de la serie temporal es traducida en la predicción
del siguiente estado del sistema. Este modelo de espacio de estados, junto con el método
de los delays (coordenadas demoradas) tiene importancia práctica en el desarrollo de este
trabajo, especÃficamente, en el diseño de la capa de entrada en algunas redes (los perceptrones
multicapa) y otros parámetros (los taps de las redes TLFN). Adicionalmente, el resto
de los componentes de la red con determinados en varios casos a través de procedimientos
tradicionalmente usados en las redes neuronales: los algoritmos genéticos.
Los criterios para la selección de modelo (red) son discutidos y un balance entre performance
y complejidad de la red es explorado luego, inspirado en el minimum description length de
Rissanen y su estimación dada por el software elegido.
Con respecto a los modelos de red empleados, las topologóas de sugeridas en la literatura
como adecuadas para la predicción son usadas (TLFNs y redes recurrentes) junto con perceptrones
multicapa (un clásico de las redes neuronales) y comités de redes. La efectividad
de cada método es confirmada por el problema de predicción propuesto. Los comités de
redes, donde las predicciones son una combinación convexa de las predicciones dadas por
las redes individuales, son también usados extensamente.
La necesidad de criterios para comparar el comportamiento del modelo con el del sistema
real, a largo plazo, para un sistema dinámico estocástico, es presentada y dos alternativas
son comentadas.
Los resultados obtenidos prueban la existencia de una solución al problema del aprendizaje
de la dependencia Entrada - Salida . Conjeturamos además que el sistema generador de
serie de las ventas es dinámico-estocástico pero no caótico, ya que sólo tenemos una realización del proceso aleatorio correspondiente a las ventas. Al ser un sistema no caótico, la media de las predicciones de las ventas deberÃa mejorar a medida que los datos disponibles
aumentan, aunque la probabilidad de una predicción con un gran error es siempre no nula debido
a la aleatoriedad presente. Esta solución es encontrada en una forma constructiva
y exhaustiva. La exhaustividad puede deducirse de las siguiente cinco afirmaciones :
el diseño de una red neuronal requiere conocer la dimensión de la entrada y de la
salida, el número de capas ocultas y las neuronas en cada una de ellas
el uso del teorema de takens-Mañé permite derivar la dimensión de la entrada
por teoremas tales como los de Kolmogorov y Cybenko el uso de perceptrones con solo
una capa oculta es justificado, por lo que varios de tales modelos son probados
el número de neuronas en la capa oculta es determinada varias veces heurÃsticamente
a través de algoritmos genéticos
una sola neurona de salida da la predicción deseada. Como se dijo, dos tareas son llevadas a cabo: el desarrollo de un modelo para la predicción de la serie temporal y el análisis de un modelo factible para la reconstrucción dinámica del sistema. Con el mejor modelo predictivo, obtenido por el comité de dos redes se logró obtener un error aceptable en la predicción de una semana no contigua al conjunto de
entrenamiento (7.04% para la semana 46/2011). Creemos que este es un resultado aceptable
dada la cantidad de información disponible y representa una validación adicional de que las
redes neuronales son útiles para la predicción de series temporales provenientes de sistemas
dinámicos, sin importar si son estocásticos o no.
Finalmente, los resultados experimentales confirmaron algunos hechos ya conocidos (tales
como que agregar ruido a los datos de entrada y de salida de los valores de entrenamiento
puede mejorar los resultados: que las redes recurrentes entrenadas con el algoritmo de
retropropagación no presentan el problema del gradiente evanescente en periodos cortos y
que el uso de de comités - que puede ser visto como una forma muy básica de inteligencia
artificial distribuida - permite mejorar significativamente las predicciones)
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