Συγκριτική μελέτη γραμμικών μοντέλων και νευρωνικών δικτύων για την λήψη ορθολογικών αποφάσεων = Comparative study of linear models and neural networks for rational decision making.

Χρήστος Ι. Τζώτζης

Συγκριτική μελέτη γραμμικών μοντέλων και νευρωνικών δικτύων για την λήψη ορθολογικών αποφάσεων = Comparative study of linear models and neural networks for rational decision making.

Χρήστος Ι. Τζώτζης

Περίληψη

Τα τεχνητά νευρωνικά δίκτυα αποτελούν, μέχρι σήμερα, μια πολύ δημοφιλή διαδικασία προσδιορισμού της βέλτιστης λύσης διάφορων προβλημάτων. Στην παρούσα εργασία κατασκευάζονται δώδεκα διαφορετικά τεχνητά νευρωνικά δίκτυα και τέσσερα γραμμικά μοντέλα με σκοπό τον έλεγχο των περιορισμών των ορθολογικών αποφάσεων που λαμβάνονται από αυτά. Τα μοντέλα αυτά εφαρμόζονται στην πρόβλεψη της μέσης τιμής ακινήτων στα προάστια της Βοστώνης και μελετώνται οι προβλέψεις που κάνουν σε σχέση με τις πραγματικές τιμές. Η αξιολόγηση των μοντέλων αυτών έγινε με τη μέθοδο της επαναλαμβανόμενης διασταυρωμένης επικύρωσης, ενώ για την σύγκρισή τους χρησιμοποιήθηκαν η ρίζα του μέσου τετραγωνικού σφάλματος και ο συντελεστής προσδιορισμού. Από τα αποτελέσματα που προκύπτουν, εξάγεται το συμπέρασμα πως τόσο τα γραμμικά μοντέλα, όσο και τα νευρωνικά δίκτυα, είναι σε θέση να λάβουν μία ικανοποιητική αλλά και μία ορθολογική απόφαση. Η σύγκριση των μοντέλων αναδεικνύει το τεχνητό νευρωνικό δίκτυο ως το καταλληλότερο μοντέλο πρόβλεψης για το πείραμα.

Artificial neural networks are, to date, a very popular process of identifying the best solution for various problems. In the present study twelve different artificial neural networks and four linear models are constructed to control the limitations of rational decisions taken by them. These models are applied to prediction of the average property price in Boston suburbs and their predictions are made relative to real prices. The evaluation of these models was done by the repeated cross-validation method, while for comparison, the root mean square error and the coefficient of determination were used. From the results obtained, it is concluded that both linear models and artificial neural networks are able to obtain a satisfactory and also a rational decision. The comparison of models highlights the artificial neural network as the most appropriate prediction model for the experiment.

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