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Application of neural networks in the identification of morphological types (CROSBI ID 28345)

Prilog u knjizi | izvorni znanstveni rad

Prot, F. ; Bosnar, K. ; Hošek, A. ; Momirović, K. Application of neural networks in the identification of morphological types // Konstrukcija i primena taksonomskih neuronskih mreža / Momirović, K. ; Popović, D.A. (ur.). Leposavić: Univerzitet u Prištini, 2003. str. 245-254-x

Podaci o odgovornosti

Prot, F. ; Bosnar, K. ; Hošek, A. ; Momirović, K.

engleski

Application of neural networks in the identification of morphological types

A sample of 737 healthy males, 19 to 27 years old, fairly representative for the Yugoslav population of this age and gender, was described over a set of 23 morphological characteristics selected to assess factors of longitudinal and transversal dimensions of skeleton, muscular mass and fat tissue. An algorithm for a neural network for cluster analysis named Triatlon was applied in order to detect the morphological types. The essence of the applied clustering algorithm is a taxonomic neural network based on adaptive multilayer perceptron as a core engine working on a basis of starting classification obtained by a rational method of fuzzy clusters of variables. Triatlon conclude that five clusters are necessary and sufficient for the taxonomic description of this data set, and that by only three hidden neurons can produce an excellent fuzzy classification of objects. After 15 iterations Triatlon produce an excellent classification of variables, but initial fuzzy clustering of objects is obtained after 71 iterations. However, multilayer perceptron consider this clkassification as good, but not satisfactory, and start learning process in order to obtain a better classification. The final classification is obtained after 24 learning attempts. However, coefficient of efficacy of Triatlon in this case was only 0.920, markedly lower than in applications of this program in other taxonomic problems. In spite of complex position of types in the space of manifest morphological characteristics and not always clear pattern and structure of discriminant factors, obtained types can be identified as follows: (1) Typus asthenicus, defined by low development of skeleton, low muscular mass, and low fat tissue ; (2) Typus sthenicus, defined by strong development of skeleton, high amount of muscular mass, and above average fat tissue due to high amount of fat cells ; (3) Typus gracilis, defined primarily by small measures of transversal dimensions of skeleton ; (4) Typus disharmonicus, defined by inconvergent development of morphological characteristics and low fat tissue ; (5) Typus leptomorphycus, defined by above average development of longitudinal dimensions of skeleton.

morphological types, neural networks, cluster analysis

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Podaci o prilogu

245-254-x.

objavljeno

Podaci o knjizi

Konstrukcija i primena taksonomskih neuronskih mreža

Momirović, K. ; Popović, D.A.

Leposavić: Univerzitet u Prištini

2003.

86-82329-21-2

Povezanost rada

Psihologija