Iris and Backpropagation
IRIS DATA ANALYSIS USING BACK PROPAGATION NEURAL NETWORKS
Sean Van Osselaer
Murdoch University, Western Australia
ABSTRACT
This project paper refers to experiments towards the classification of Iris plants with back propagation neural networks (BPNN). The problem concerns the identification of Iris plant species on the basis of plant attribute measurements. The paper outlines background information concerning the problem, making reference to statistics and value constraints identified in the course of the project. There is an outline of the algorithm of techniques used within the project, with descriptions of these techniques and their context. A discussion concerning the experimental setup is included, describing the implementation specifics of the project, preparatory actions, and the experimental results. The results generated by the networks constructed are presented, with the results being discussed and compared towards identification of the fittest architecture for the problem constrained by the data set. In conclusion, the fittest architecture is identified, and a justification concerning its selection offered.
Keywords : Iris, back propagation neural network, BPNN
INTRODUCTION
This project paper is related to the use of back propagation neural networks (BPNN) towards the identification of iris plants on the basis of the following measurements: sepal length, sepal width, petal length, and petal width. There is a comparison of the fitness of neural networks with input data normalised by column, row, sigmoid, and column constrained sigmoid normalisation. Also contained within the paper is an analysis of the performance results of back propagation neural networks with various numbers of hidden layer neurons, and differing number of cycles (epochs). The analysis of the performance of the neural networks is based on several criteria: incorrectly identified plants by training set (recall) and testing set (accuracy), specific error within
Sean Van Osselaer
Murdoch University, Western Australia
ABSTRACT
This project paper refers to experiments towards the classification of Iris plants with back propagation neural networks (BPNN). The problem concerns the identification of Iris plant species on the basis of plant attribute measurements. The paper outlines background information concerning the problem, making reference to statistics and value constraints identified in the course of the project. There is an outline of the algorithm of techniques used within the project, with descriptions of these techniques and their context. A discussion concerning the experimental setup is included, describing the implementation specifics of the project, preparatory actions, and the experimental results. The results generated by the networks constructed are presented, with the results being discussed and compared towards identification of the fittest architecture for the problem constrained by the data set. In conclusion, the fittest architecture is identified, and a justification concerning its selection offered.
Keywords : Iris, back propagation neural network, BPNN
INTRODUCTION
This project paper is related to the use of back propagation neural networks (BPNN) towards the identification of iris plants on the basis of the following measurements: sepal length, sepal width, petal length, and petal width. There is a comparison of the fitness of neural networks with input data normalised by column, row, sigmoid, and column constrained sigmoid normalisation. Also contained within the paper is an analysis of the performance results of back propagation neural networks with various numbers of hidden layer neurons, and differing number of cycles (epochs). The analysis of the performance of the neural networks is based on several criteria: incorrectly identified plants by training set (recall) and testing set (accuracy), specific error within
References: [1] Negnevitsky, M. Artificial Intelligence: A Guide to Intelligent Systems, First Edition, Harlow, Pearson Eduction, 2002, Page 175-178. [2] Chung, E. Kusiak, A. Grouping parts with a neural network, “Journal of Manufacturing Systems[Full Text], volume 13, issue 4, Page 262, Available ProQuest, 02786125 , 20-04-2003”.