# ANN - A Brief Introduction to Genetic A Brief Introduction...

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Unformatted text preview: A Brief Introduction to Genetic A Brief Introduction to Genetic Algorithms, Artificial Neural Algorithms, Artificial Neural Networks and Fuzzy Logic Networks and Fuzzy Logic Prof. Daniel Pete Loucks Donato da Silva Filho Visiting Scholar CORNELL U N I V E R S I T Y School of Civil and Environmental Engineering CEE 593 - Engineering Management Methods I General Overview ■ The tutorial is divided in three main topics: c Genetic Algorithms. – Artificial Neural Networks. – Fuzzy Logic. ■ Each topic is organized as follows: – Brief theoretical background. – Simple numerical examples. – Engineering application. Examples ■ Artificial Neural Networks : Recognition of patterns in data. c Stock market analyses and forecasting. c Credit evaluation. c Diagnose of diseases ( medical images ). i Water inflow forecasting for water resource systems. c Modeling of nonlinear systems. c Robots and autonomous systems. Illustration of the process Set of Data 1 2 We know these data are correlated, but we have no idea about the model. 3 We can distinguish between input (cause) and output (effect). 4 An Artificial Neural Network can be used to “learn” the model. The “black box” Artificial Neural Network Input Output What is inside the box? A single neuron ■ Our brain has ten millions of neurons, which are connected through a very complex network. ■ Each neuron receives a lot of input signals. ■ Signals are “summed up” in some way. ■ If the amount of signal is higher than a threshold value, the neuron activates its output. ■ There is no physical connection between the axon of a neuron and the dendrites of another one. ■ The connection is chemical: the amount of neurotransmitters delivered by the synapses. ■ The learning process happens when the synapse connections change the way they deliver neurotransmitters....
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ANN - A Brief Introduction to Genetic A Brief Introduction...

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