Multilayer Perceptron (MLP)

The primary aim of the study is to introduce APLOCO method which is developed for the solution of multi-criteria decision making problems both theoretically and practically. In this context, application subject of APLACO constitutes evaluation of investment potential of different cities in metropolitan status in Turkey. The secondary purpose of the study is to identify the independent variables affecting the factories in the operating phase and to estimate the effect levels of independent variables on the dependent variable in the organized industrial zones (OIZs), whose mission is to reduce regional development disparities and to mobilize local production dynamics. For this purpose, the effect levels of independent variables on dependent variables have been determined using the multilayer perceptron (MLP) method, which has a wide use in artificial neural networks (ANNs). The effect levels derived from MLP have been then used as the weight levels of the decision criteria in APLOCO. The independent variables included in MLP are also used as the decision criteria in APLOCO. According to the results obtained from APLOCO, Istanbul city is the best alternative in term of the investment potential and other alternatives are Manisa, Denizli, Izmir, Kocaeli, Bursa, Ankara, Adana, and Antalya, respectively. Although APLOCO is used to solve the ranking problem in order to show application process in the paper, it can be employed easily in the solution of classification and selection problems. On the other hand, the study also shows a rare example of the nested usage of APLOCO which is one of the methods of operation research as well as MLP used in determination of weights. KEYWORDS approach of logarithmic concept (APLOCO), multi criteria decision analysis (MCDA), multilayer perceptron (MLP), organized industrial zones (OIZs), artificial intelligence, artifical neural networks (ANNs)

In this report, two learning methods of a multilayer perceptron neural network have been studied. We apply Levenberg-Marquardt and error backpropagation (EBP) methods on the vision system of Small Size League (SSL) robots to find their directions on the field. The results are compared over different training sets.