Comparação Entre as Redes Neurais Adaline E Perceptron Utilizando O Conjunto De Dados Iris

Abstract

Initially, neural networks were developed with the objective of creating a computational system that models the functioning of the human brain, however they started to be used to solve specific tasks. Adaline and Perceptron are two neural networks that calculate an input function using a set of adaptive weights and a bias, despite their similarities, it is known that the Adaline neural network converges to a result more quickly than the Perceptron neural network. This work was designed as a didactic exercise, in order to present how such conclusions are obtained, using the IRIS database as data for classification and training. Throughout the work, the programming languages Processing, was used to develop neural networks, and Python for visual presentation of results. The results found show the high performance of the Adaline neural network over the Perceptron, showing the database classes that can be linearly separated and those that cannot, the metric used to evaluate the performance between the neural networks is defined by the percentage of correct answers in the data classifications. Adaline showed the best performance in the classification for length and width of the petal between the Iris-setosa and Iris-virginica classes among all the other classifications.