Intercriteria analysis (ICA) is a new method, which is based on the concepts of index matrices and intuitionistic fuzzy sets, aiming\nat detection of possible correlations between pairs of criteria, expressed as coefficients of the positive and negative consonance\nbetween each pair of criteria. Here, the proposed method is applied to study the behavior of one type of neural networks, the\nmodular neural networks (MNN), that combine several simple neuralmodels for simplifying a solution to a complex problem.They\nare a tool that can be used for object recognition and identification. Usually the inputs of the MNN can be fed with independent\ndata. However, there are certain limits when we may use MNN, and the number of the neurons is one of the major parameters\nduring the implementation of theMNN. On the other hand, a high number of neurons can slow down the learning process, which\nis not desired. In this paper, we propose a method for removing part of the inputs and, hence, the neurons, which in addition leads\nto a decrease of the error between the desired goal value and the real value obtained on the output of the MNN. In the research\nwork reported here the authors have applied the ICA method to the data fromreal datasets with measurements of crude oil probes,\nglass, and iris plant.The method can also be used to assess the independence of data with good results.
Loading....