Neural networks and spectra feature selection for retrival of hot gases temperature profiles

Proceeding of: International Conference on Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Vienna, Austria 28-30 Nov. 2005Neural networks appear to be a promising tool to solve the so-called inverse problems focused to obtain a retrieval of certain physical properties related to the radiative transference of energy. In this paper the capability of neural networks to retrieve the temperature profile in a combustion environment is proposed. Temperature profile retrieval will be obtained from the measurement of the spectral distribution of energy radiated by the hot gases (combustion products) at wavelengths corresponding to the infrared region. High spectral resolution is usually needed to gain a certain accuracy in the retrieval process. However, this great amount of information makes mandatory a reduction of the dimensionality of the problem. In this sense a careful selection of wavelengths in the spectrum must be performed. With this purpose principal component analysis technique is used to automatically determine those wavelengths in the spectrum that carry relevant information on temperature distribution. A multilayer perceptron will be trained with the different energies associated to the selected wavelengths. The results presented show that multilayer perceptron combined with principal component analysis is a suitable alternative in this field.Publicad

Grammars and cellular automata for evolving neural networks architectures

IEEE International Conference on Systems, Man, and Cybernetics. Nashville, TN, 8-11 October 2000The class of feedforward neural networks trained with back-propagation admits a large variety of specific architectures applicable to approximation pattern tasks. Unfortunately, the architecture design is still a human expert job. In recent years, the interest to develop automatic methods to determine the architecture of the feedforward neural network has increased, most of them based on the evolutionary computation paradigm. From this approach, some perspectives can be considered: at one extreme, every connection and node of architecture can be specified in the chromosome representation using binary bits. This kind of representation scheme is called the direct encoding scheme. In order to reduce the length of the genotype and the search space, and to make the problem more scalable, indirect encoding schemes have been introduced. An indirect scheme under a constructive algorithm, on the other hand, starts with a minimal architecture and new levels, neurons and connections are added, step by step, via some sets of rules. The rules and/or some initial conditions are codified into a chromosome of a genetic algorithm. In this work, two indirect constructive encoding schemes based on grammars and cellular automata, respectively, are proposed to find the optimal architecture of a feedforward neural network

Studying the capacity of cellular encoding to generate feedforward neural network topologies

Proceeding of: IEEE International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 25-29 July 2004Many methods to codify artificial neural networks have been developed to avoid the disadvantages of direct encoding schema, improving the search into the solution's space. A method to analyse how the search space is covered and how are the movements along search process applying genetic operators is needed in order to evaluate the different encoding strategies for multilayer perceptrons (MLP). In this paper, the generative capacity, this is how the search space is covered for a indirect scheme based on cellular systems, is studied. The capacity of the methods to cover the search space (topologies of MLP space) is compared with the direct encoding scheme.Publicad

Neural Network architectures design by Cellular Automata evolution

4th Conference of Systemics Cybernetics and Informatics. Orlando, 23-26 July 2000The design of the architecture is a crucial step in the successful application of a neural network. However, the architecture design is basically, in most cases, a human experts job. The design depends heavily on both, the expert experience and on a tedious trial-and-error process. Therefore, the development of automatic methods to determine the architecture of feedforward neural networks is a field of interest in the neural network community. These methods are generally based on search techniques, as genetic algorithms, simulated annealing or evolutionary strategies. Most of the designed methods are based on direct representation of the parameters of the network. This representation does not allow scalability, so to represent large architectures very large structures are required. In this work, an indirect constructive encoding scheme is proposed to find optimal architectures of feed-forward neural networks. This scheme is based on cellular automata representations in order to increase the scalability of the method.Publicad

Generative capacities of cellular automata codification for evolution of NN codification

Proceeding of: International Conference on Artificial Neural Networks. ICANN 2002, Madrid, Spain, August 28-30, 2002Automatic methods for designing artificial neural nets are desired to avoid the laborious and erratically human expert’s job. Evolutionary computation has been used as a search technique to find appropriate NN architectures. Direct and indirect encoding methods are used to codify the net architecture into the chromosome. A reformulation of an indirect encoding method, based on two bi-dimensional cellular automata, and its generative capacity are presented.Publicad

La ayuda oficial al desarrollo hacia Extremo Oriente y América Central y el Caribe : un analisis comparativo

En este trabajo estudiamos los flujos de ayuda oficial al desarrollo hacia los países en desarrollo, como se distribuyen por grandes regiones y quienes la dan, centrando la atención en el Extremo Oriente y América Central y El Caribe. Todo ello referido al periodo que va de 1980 a la actualidad. En paralelo, comparamos con los flujos de inversión directa que van hacia esos países, intentando con ello una primera aproximación al análisis de la mayor o menor pertinencia de cada uno de ellos como instrumento para el desarrollo de esos países. ____________________________________________In this paper we study the flows of official development assistance to developing countries, as distributed by major region and those who give, focusing on the Far East and Central America and the Caribbean. This referred to the period from 1980 to present. At the same time, ODA flows are compared to direct investment flows going to these countries, thereby a first approach to the analysis of the degree of relevance of each of them as a tool for development of those countries is attempted

Evolutionary cellular configurations for designing feed-forward neural networks architectures

Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward neural networks has increased. Most of the methods are based on evolutionary computation paradigms. Some of the designed methods are based on direct representations of the parameters of the network. These representations do not allow scalability, so to represent large architectures, very large structures are required. An alternative more interesting are the indirect schemes. They codify a compact representation of the neural network. In this work, an indirect constructive encoding scheme is presented. This scheme is based on cellular automata representations in order to increase the scalability of the method

Space Vector Modulation Techniques for Multilevel Converters – a survey

This paper presents a survey of most recent, simple and efficient Space Vector Modulation algorithms for multilevel converters. These algorithms avoid trigonometric and other complex operations, leading to more simple and cost efficient implementations. They can be applied to multilevel topologies and present freedom degrees that can be Exploited in order to optimize system parameters in the system like: capacitors voltages balancing or voltage/current ripples. Experimental results are presented to show the good performance of the algorithms