Simulation of Back Propagation Neural Network for Iris Flower Classification
نویسندگان
چکیده
One of the most dynamic research and application areas of neural networks is classification. In this paper, the use of matlab coding for simulation of backpropagation neural network for classification of Iris dataset is demonstrated. Fisher’s Iris data base collected from uci repository is used. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. Sepal length, sepal width, petal length and petal width are the four features used to classify each flower to its category. The three classes of the flower are Iris Setosa, Iris Versicolor and Iris Verginica. The network is trained for different epochs with different number of neurons in the hidden layer. The performance of the network is evaluated by plotting the error versus the number of iterations, furthermore by testing the network with different samples of the iris flower data. The successfully trained network classified the testing data correctly; indicating 100% recognition.
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