This paper introduces a flexible neural tree (FNT) with necessary number of hidden units and is generated initially as a flexible multi-layer feed-forward neural network evolved using an evolutionary procedure and also considers the approximation of sufficiently smooth multivariable function with a multilayer perceptron. For a given neural tree with approximation order, explicit formulas for th...

In recent years, Educational Data Mining has put on a massive appreciation within the research realm and it has become a vital need for the academic institutions to improve the quality of education. The quality of education is measured by the academic performance of students and the results produced. In higher education institutions a substantial amount of knowledge is hidden and need to be ext...

this paper is based on a combination of the principal component analysis (pca), eigenface and support vector machines. using n-fold method and with respect to the value of n, any person’s face images are divided into two sections. as a result, vectors of training features and test features are obtain ed. classification precision and accuracy was examined with three different types of kernel and...

This paper presents transient stability assessment of electrical power system using probabilistic neural network (PNN) and principle component analysis. Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on th...

This paper presents a comparative study of different methods for the identification of multiword expressions, applied to a Brazilian Portuguese corpus. First, we selected the candidates based on the frequency of bigrams. Second, we used the linguistic information based on the grammatical classes of the words forming the bigrams, together with the frequency information in order to compare the pe...

Recently, the authors described a training method for a convolutional neural network of threshold neurons. Hidden layers are trained by by clustering, in a feed-forward manner, while the output layer is trained using the supervised Perceptron rule. The system is designed for implementation on an existing low-power analog hardware architecture, exhibiting inherent error sources affecting the com...

Bankruptcy prediction is an important classification problem for a business, and has become a major concern of managers. In this paper, two well-known backpropagation neural network models serving as data mining tools for classification problems are employed to perform bankruptcy forecasting: one is the backpropagation multi-layer perceptron, and the other is the radial basis function network. ...

Many algorithms have been recently reported for the training of analog multi-layer perceptron. Most of these algorithms were evaluated either from a computational or simulation view point. This paper applies several of these algorithms to the training of an analog multi-layer perceptron chip. The advantages and shortcomings of these algorithms in terms of training and generalisation performance...

In this article we are going to discuss the improvement of the multi classes’ classification problem using multi layer Perceptron. The considered approach consists in breaking down the n-class problem into two-classes’ subproblems. The training of each two-class subproblem is made independently; as for the phase of test, we are going to confront a vector that we want to classify to all two clas...