Here the authors propose the use of Fuzzy Multilayer Perceptrons for classification of land use and land cover patterns in the Brazilian Amazon, using time series of vegetation index, taken from NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. In addition to the traditional Multilayer Perceptron (MLP), three fuzzy implementations were investigated. These methods were applied...

The relation between classifier complexity and learning set size is very important in discriminant analysis. One of the ways to overcome the complexity control problem is to add noise to the training objects, increasing in this way the size of the training set. Both the amount and the directions of noise injection are important factors which determine the effectiveness for classifier training. ...

In this paper, we present a comparative study of several methods that combine evolutionary algorithms and local search methods to optimize multilayer perceptrons: A method that optimizes the architecture and initial weights of multilayer perceptrons; another that searches for training algorithm parameters, and finally, a co-evolutionary algorithm, introduced in this paper, that handles the arch...

In this paper, a key swap over mechanism among group of multilayer perceptrons for encryption/decryption (KSOGMLPE) has been proposed in wireless communication of data/information. Two parties can swap over a common key using synchronization between their own multilayer perceptrons. But the problem crop up when group of N parties desire to swap over a key. Since in this case each communicating ...

Necessary and sufficient conditions for implementing particular decision regions by multi-layer perceptrons have been presented in recent studies. In this paper, from a viewpoint of engineering, a constructive algorithm is proposed to implement celled decision regions using two-layer perceptrons without any training procedure. The algorithm examines the feasibility of a celled decision region a...

The aim of this report was to implement a text-dependent speaker verification system using speaker adapted neural networks and to evaluate the system. The idea was to use a hybrid HMM/ANN approach, i.e. Artificial Neural Networks were used to estimate Hidden Markov Model emission posterior probabilities from speech data, and the system was implemented in C++ as a module for GIVES. The report al...

Multilayer perceptrons and radial basis function networks are used most often in classification tasks, even though the two neural networks have different performance in classification tasks depending on the available training data sets. This paper shows the accuracy change in classification of the two neural networks when training data set size changes. Experiments were run with four data sets ...

Fagin et al. characterized those symmetric Boolean functions which can be computed by small AND/OR circuits of constant depth and unbounded fan-in. Here we provide a similar characterization for d-perceptrons | AND/OR circuits of constant depth and unbounded fan-in with a single MAJORITY gate at the output. We show that a symmetric function has small (quasipolynomial, or 2 log O(1) n size) d-pe...

Etienne Barnard Carnegie-Mellon University Multi-layer perceptrons and trained classification trees are two very different techniques which have recently become popular. Given enough data and time, both methods are capable of performing arbitrary non-linear classification. We first consider the important differences between multi-layer perceptrons and classification trees and conclude that ther...

It’s well known that the computing time to train multilayer perceptrons is very long because of weight space of the neural networks and small amount of adjustment of the wiights for convergence. The matter becomes worse when the size of training data set is large, which is common in data mining tasks. Moreover, depending on samples, the performance of neural networks change. So, in order to det...