An infrastructure for neural network construction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.After many years of research the area of Artificial Intelligence is still searching for ways to construct a truly intelligent system. One criticism is that current models are not 'rich' or complex enough to operate in many and varied real world situations. One way to tackle this criticism is to look at intelligent systems that already exist in nature and examine these to determine what complexities exist in these systems and not in the current Al models. The research begins by presenting an overview of the current knowledge of Biological Neural Networks, as examples of intelligent systems existing in nature, and how they function. Artificial Neural networks are then discussed and the thesis examines their similarities and dissimilarities with their biological counterparts. The research suggests ways that Artificial Neural Networks may be improved by borrowing ideas from Biological Neural Networks. By introducing new concepts drawn from the biological realm, the construction of the Artificial Neural Networks becomes more difficult. To solve this difficulty, the thesis introduces the area of Evolutionary Algorithms as a way of constructing Artificial Neural Networks. An intellectual infrastructure is developed that incorporates concepts from Biological Neural Networks into current models of Artificial Neural Networks and two models are developed to explore the concept that increased complexity can indeed add value to the current models of Artificial Neural Networks. The outcome of the thesis shows that increased complexity can have benefits in terms of learning speed of an Artificial Neural Network and in terms of robustness to damage

Product Unit Learning

Product units provide a method of automatically learning the higher-order input combinations required for the efficient synthesis of Boolean logic functions by neural networks. Product units also have a higher information capacity than sigmoidal networks. However, this activation function has not received much attention in the literature. A possible reason for this is that one encounters some problems when using standard backpropagation to train networks containing these units. This report examines these problems, and evaluates the performance of three training algorithms on networks of this type. Empirical results indicate that the error surface of networks containing product units have more local minima than corresponding networks with summation units. For this reason, a combination of local and global training algorithms were found to provide the most reliable convergence. We then investigate how `hints' can be added to the training algorithm. By extracting a common frequency from the input weights, and training this frequency separately, we show that convergence can be accelerated. A constructive algorithm is then introduced which adds product units to a network as required by the problem. Simulations show that for the same problems this method creates a network with significantly less neurons than those constructed by the tiling and upstart algorithms. In order to compare their performance with other transfer functions, product units were implemented as candidate units in the Cascade Correlation (CC) \cite{Fahlman90} system. Using these candidate units resulted in smaller networks which trained faster than when the any of the standard (three sigmoidal types and one Gaussian) transfer functions were used. This superiority was confirmed when a pool of candidate units of four different nonlinear activation functions were used, which have to compete for addition to the network. Extensive simulations showed that for the problem of implementing random Boolean logic functions, product units are always chosen above any of the other transfer functions. (Also cross-referenced as UMIACS-TR-95-80

An infrastructure for neural network construction

After many years of research the area of Artificial Intelligence is still searching for ways to construct a truly intelligent system. One criticism is that current models are not 'rich' or complex enough to operate in many and varied real world situations. One way to tackle this criticism is to look at intelligent systems that already exist in nature and examine these to determine what complexities exist in these systems and not in the current Al models. The research begins by presenting an overview of the current knowledge of Biological Neural Networks, as examples of intelligent systems existing in nature, and how they function. Artificial Neural networks are then discussed and the thesis examines their similarities and dissimilarities with their biological counterparts. The research suggests ways that Artificial Neural Networks may be improved by borrowing ideas from Biological Neural Networks. By introducing new concepts drawn from the biological realm, the construction of the Artificial Neural Networks becomes more difficult. To solve this difficulty, the thesis introduces the area of Evolutionary Algorithms as a way of constructing Artificial Neural Networks. An intellectual infrastructure is developed that incorporates concepts from Biological Neural Networks into current models of Artificial Neural Networks and two models are developed to explore the concept that increased complexity can indeed add value to the current models of Artificial Neural Networks. The outcome of the thesis shows that increased complexity can have benefits in terms of learning speed of an Artificial Neural Network and in terms of robustness to damage.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Aplicaciones de modelos predictivos en evaluación de riesgo de "Listeria monocytogenes" en productos mínimamente procesados

En la presente Tesis Doctoral se estudia el comportamiento de un microorganismo patógeno de interés alimentario. Listeria monocytogenes, a través del desarrollo de diferentes tipos de modelos predictivos y su posterior aplicación en un esquema de Evaluación Cuantitativa del Riesgo Microbiano (ECRM). En primer lugar, se procede a la estimación de los principales parámetros de crecimiento microbiano (fase de latencia, tasa máxima de crecimiento y densidad máxima de población), así como la probabilidad de crecimiento bajo diferentes condiciones ambientales sobre medios de cultivo. Posteriormente se monitoriza la calidad microbiológica y sensorial de espárragos blancos pasteurizados a diferentes temperaturas de almacenamiento, junto con la incidencia y evolución de crecimiento de L.monocytogenes. A través de la información obtenida, y junto con una recopilación de datos procedentes de la literatura científica, se aborda la aplicación de los modelos predictivos desarrollados en un esquema de ECRM mediante la estimación del número final de células presentes en el momento de consumo y el número de casos/año sobre una población. La metodología seguida en la presente Tesis doctoral constituye una vía relativamente sencilla de evaluar la seguridad microbiológica de un proceso alimentario mediante la integración modelos matemáticos dentro de un esquema de EC