We propose a new learning algorithm for multi Multi Layered Perceptron (MLP) architecture useful for complex classification. First, on the principle “divide and conquer”, a quantization is performed on the input space. One classical Neural Network (NN) is devoted to each subregion that results from this quantization. A cooperative learning procedure allows to train the NNs in such a way that NN...

The performance of a recognition system depends upon the classifiers used for classification purpose. Powerful is the discrimination ability of a classifier, better is its recognition performance. The generalization ability of a classifier is measured on the basis of its performance in classifying the test patterns. There are various factors which affect generalization. Moreover, the feature ex...

Artificial Neural Network is widely used to learn data from systems for different types of applications. The capability of different types of Integrated Circuit (IC) based ANN structures also depends on the hardware backbone used for their implementation. In this work, Field Programmable Gate Array (FPGA) based Multilayer Perceptron Artificial Neural Network (MLP-ANN) neuron is developed. Exper...

Face recognition is a method for verifying or identifying a person from a digital image. In this paper an approach for classifying images based on discrete wavelet transform (DWT) and neural network (NN) has been suggested. In the proposed approach, DWT decomposes an image into images with different frequency bands. An NN is a trainable and dynamic system which can acceptably estimate input-out...

Neural network (NN) methods are sometimes useless in practical applications, because they are not properly tailored to the particular market’s needs. We focus thereinafter specifically on financial market applications. NNs have not gained full acceptance here yet. One of the main reasons is the “Black Box” problem (lack of the NN decisions explanatory power). There are though some NN decisions ...

Neural network (NN) methods are sometimes useless in practical applications, because they are not properly tailored to the particular market’s needs. We focus thereinafter specifically on financial market applications. NNs have not gained full acceptance here yet. One of the main reasons is the “Black Box” problem (lack of the NN decisions explanatory power). It remains an open issue for the to...

In this letter, we demonstrate that the generalization properties of a neural network (NN) can be extended to encompass objects that obscure or segment the original image in its foreground or background. We achieve this by piloting an extension of the noise injection training technique, which we term excessive noise injection (ENI), on a simple feedforward multilayer perceptron (MLP) network wi...

We investigated the usability of the method of local linear models (LLM), multilayer perceptron neural network (MLP NN) and radial basis function neural network (RBF NN) for the construction of temporal and spatial transfer functions between different meteorological quantities, and compared the obtained results both mutually and to the results of multiple linear regression (MLR). The tested met...

In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for i...