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| Author(s): |
Antonio Nilder Duarte Furtado
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC/SBD) |
| Defense date: | 1998-07-31 |
| Examining board members: |
Eiji Kawamoto;
André Carlos Ponce de Leon Ferreira de Carvalho;
Simin Jalali Rahnemay Rabbani;
Suely da Penha Sanches;
José Reynaldo Anselmo Setti
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| Advisor: | Eiji Kawamoto |
| Abstract | |
This thesis presents a research aimed at the use of Artificial Neural Networks (ANN) for appraising and ranking transportation project alternatives. Based on the principle that this process of appraisal and ranking constitutes a pattern that can be perceived by ANN, the verification of this hypothesis was conducted selecting an evaluation context, defining variables to be considered in the process, and creating ANN structures for training based on other evaluation cases. In this research, 180 \"Case Studies\" from 32 American states were used. These data were used as input to a learning process using the simulator \"Neural Planner 4.52\", which is based on \"Multilayer Perceptron (MLP)\" networks and uses a \"Backpropagation\" training algorithm. Several networks were trained to obtain the one most capable of recognizing the patterns of the projects analyzed. More than 92% of the 486 experiments presented right indexes, as shown by a software called \"EVALUATOR\", a user interface between ANN simulator. The conclusion is that ANN can recognize the implicit patterns in previous evaluations and can be used to appraise and rank alternatives from other projects belonging to the same context used for the ANN training. (AU) | |