Abstrakt

Identification of Iris Plant Using Feed Forward Neural Network On The Basis Of Floral Dimensions

Shrikant Vyas , Dipti Upadhyay

The categorization and recognition of type on the basis of individual characteristics and behaviors form a preliminary measure and is an important target in the behavioural sciences. Current statistical methods do not always give satisfactory results. A Feed Forward Artificial Neural Network is the computer model inspired by the structure of the Human Brain. It views as in the set of artificial nerve cells that are interconnected with the other neurons. The primary aim of this paper is to demonstrate the process of developing the Artificial Neural network based classifier which classifies the Iris database. The problem concerns the identification of Iris plant species on the basis of plant attribute measurements. This paper is related to the use of feed forward neural networks towards the identification of iris plants on the basis of the following measurements: sepal length, sepal width, petal length, and petal width. Using this data set a Neural Network (NN) is used for the classification of iris data set. The EBPA is used for training of this ANN. The results of simulations illustrate the effectiveness of the neural system in iris class identification