6 research outputs found
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GA-based learning algorithms to identify fuzzy rules for fuzzy neural networks
Identification of fuzzy rules is an important issue in
designing of a fuzzy neural network (FNN). However,
there is no systematic design procedure at present. In
this paper we present a genetic algorithm (GA) based
learning algorithm to make use of the known membership
function to identify the fuzzy rules form a large set
of all possible rules. The proposed learning algorithm
initially considers all possible rules then uses the
training data and the fitness function to perform ruleselection.
The proposed GA based learning algorithm
has been tested with two different sets of training data.
The results obtained from the experiments are promising
and demonstrate that the proposed GA based
learning algorithm can provide a reliable mechanism
for fuzzy rule selection
An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems
[[abstract]]In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF controller are demonstrated to illustrate the effectiveness of the proposed method.
Contribution to the study and design of advanced controllers : application to smelting furnaces
In this doctoral thesis, contributions to the study and design of advanced controllers and their application to metallurgical smelting furnaces are discussed. For this purpose, this kind of plants has been described in detail. The case of study is an Isasmelt plant in south Peru, which yearly processes 1.200.000 tons of copper concentrate. The current control system is implemented on a distributed control system. The main structure includes a cascade strategy to regulate the molten bath temperature. The manipulated variables are the oxygen enriched air and the oil feed rates. The enrichment rate is periodically adjusted by the operator in order to maintain the oxidizing temperature. This control design leads to large temperature deviations in the range between 15ºC and 30ºC from the set point, which causes refractory brick wear and lance damage, and subsequently high production costs.
The proposed control structure is addressed to reduce the temperature deviations. The changes emphasize on better regulate the state variables of the thermodynamic equilibrium: the bath temperature within the furnace, the matte grade of molten sulfides (%Cu) and the silica (%SiO2) slag contents. The design is composed of a fuzzy module for adjusting the ratio oxygen/nitrogen and a metallurgical predictor for forecasting the molten composition. The fuzzy controller emulates the best furnace operator by manipulating the oxygen enrichment rate and the oil feed in order to control the bath temperature.
The human model is selected taking into account the operator' practical experience in dealing with the furnace temperature (and taking into account good practices from the Australian Institute of Mining and Metallurgy). This structure is complemented by a neural network based predictor, which estimates measured variables of the molten material as copper (%Cu) and silica (%SiO2) contents. In the current method, those variables are calculated after carrying out slag chemistry assays at hourly intervals, therefore long time delays are introduced to the operation.
For testing the proposed control structure, the furnace operation has been modeled based on mass and energy balances. This model has been simulated on a Matlab-Simulink platform (previously validated by comparing real and simulated output variables: bath temperature and tip pressure) as a reference to make technical comparisons between the current and the proposed control structure. To systematically evaluate the results of operations, it has been defined some original proposals on behavior indexes that are related to productivity and cost variables. These indexes, complemented with traditional indexes, allow assessing qualitatively the results of the control comparison. Such productivity based indexes complement traditional performance measures and provide fair information about the efficiency of the control system.
The main results is that the use of the proposed control structure presents a better performance in regulating the molten bath temperature than using the current system (forecasting of furnace tapping composition is helpful to reach this improvement). The mean square relative error of temperature error is reduced from 0.72% to 0.21% (72%) and the temperature standard deviation from 27.8ºC to 11.1ºC (approx. 60%). The productivity indexes establish a lower consumption of raw materials (13%) and energy (29%).En esta tesis doctoral, se discuten contribuciones al estudio y diseño de controladores avanzados y su aplicación en hornos metalúrgicos de fundición. Para ello, se ha analizado este tipo de plantas en detalle. El caso de estudio es una planta Isasmelt en el sur de Perú, que procesa
anualmente 1.200.000 toneladas de concentrado de cobre. El sistema de control actual opera sobre un sistema de control distribuido. La estructura principal incluye una estrategia de cascada para regular la temperatura del baño. Las variables manipuladas son el aire enriquecido
con oxígeno y los flujos de alimentación de petróleo. La tasa de enriquecimiento se ajusta perióodicamente por el operador con el fin de mantener la temperatura de oxidación. Este diseño de control produce desviaciones de temperatura en el rango entre 15º C y 30º C con relación al valor de consigna, que causa desgastes del ladrillo refractario y daños a la lanza, lo cual encarece los costos de producción.
La estructura de control propuesta esta orientada a reducir las desviaciones de temperatura. Los cambios consisten en mejorar el control de las variables de estado de equilibrio termodinámico: la temperatura del baño en el horno, el grado de mata (%Cu) y el contenido de escoria en la sílice (%SiO2). El diseño incluye un módulo difuso para ajustar la proporción oxígeno/nitrógeno y un predictor metalúrgico para estimar la composición del material fundido. El controlador difuso emula al mejor operador de horno mediante la manipulación de la tasa de enriquecimiento de oxígeno y alimentación con el fin de controlar la temperatura del baño del aceite. El modelo humano es seleccionado teniendo en cuenta la experiencia del operador en el control de la temperatura del horno (y considerando el principio de buenas prácticas del Instituto Australiano de Minería y Metalurgia). Esta estructura se complementa con un predictor basado en redes neuronales, que estima las variables medidas de material fundido como cobre (%Cu) y el contenido de sílice (%SiO2). En el método actual, esas variables se calculan después de ensayos de química de escoria a intervalos por hora, por lo tanto se introducen tiempos de retardo en la operación.
Para probar la estructura de control propuesto, la operación del horno ha sido modelada en base a balances de masa y energía. Este modelo se ha simulado en una plataforma de Matlab-Simulink (previamente validada mediante la comparación de variables de salida real y lo simulado:
temperatura de baño y presión en la punta de la lanza) como referencia para hacer comparaciones técnicas entre la actual y la estructura de control propuesta.
Para evaluar sistemáticamente los resultados de estas operaciones, se han definido algunas propuestas originales sobre indicadores que se relacionan con las variables de productividad y costos. Estos indicadores, complementados con indicadores tradicionales, permite evaluar
cualitativamente los resultados de las comparativas de control. Estos indicadores de productividad complementan las medidas de desempeño tradicionales y mejoran la información sobre la eficiencia de control.
El resultado principal muestra que la estructura de control propuesta presenta un mejor rendimiento en el control de temperatura de baño fundido que el actual sistema de control. (La estimación de la composición del material fundido es de gran ayuda para alcanzar esta mejora). El error relativo cuadrático medio de la temperatura se reduce de 0,72% al 0,21% (72%) y la desviación estandar de temperatura de 27,8 C a 11,1 C (aprox. 60%). Los indicadores de productividad establecen asimismo un menor consumo de materias primas (13%) y de consumo de energía (29%)
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Intelligent Real-Time Decision Support Systems for Road Traffic Management. Multi-agent based Fuzzy Neural Networks with a GA learning approach in managing control actions of road traffic centres.
The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control