This paper shows the results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural ne...

The effectiveness of Gabor filters for texture segmentation is well known. In this paper, we propose a texture identification scheme, based on a neural network (NN) using Gabor features. The features are derived from both the Gabor cosine and sine filters. Through experiments, we demonstrate the effectiveness of a NN based classifier using Gabor features for identifying textures in a controlled...

In this letter, neural networks (NNs) classify alcoholics and nonalcoholics using features extracted from visual evoked potential (VEP). A genetic algorithm (GA) is used to select the minimum number of channels that maximize classification performance. GA population fitness is evaluated using fuzzy ARTMAP (FA) NN, instead of the widely used multilayer perceptron (MLP). MLP, despite its effectiv...

In this paper, an innovative data pre-processing method to improve the classification performance and to determine automatically the vertebral column disorders including disk hernia (DH), spondylolisthesis (SL) and normal (NO) groups has been proposed. In the classification of vertebral column disorders' dataset with three classes, a pairwise fuzzy C-means (FCM) based feature weighting method h...

In this paper, we propose a new global and fast Multilayer Perceptron Neural Network (MLP-NN) which can be used to forecast the automotive price. Nowadays, the gradient-based techniques, such as back propagation, are widely used for training neural networks. These techniques have local convergence results and, therefore, can perform poorly even on simple problems when forecasting is out of samp...

Carbon monoxide (CO) is one of the main air pollutants produced by incomplete combustion process particularly in the urban areas and exposing to the CO polluted environments will definitely affect human health. Therefore, providing a comprehensive computer modeling based on the current and previous related information for further study, analyses and decision making is of paramount importance. T...

In this paper, a bi-state Brain Computer Interface (BCI) using neural network (NN) classification of electroencephalogram (EEG) signals extracted during mental tasks has been designed. The output of the BCI design could be used with a translation scheme such as Morse Code for paralysed individuals to communicate with their external surroundings. In the experimental study, EEG signals from 5 men...

The application of neural networks (NNs) to automatic analysis of chromosome images is investigated in this paper. All aspects of the analysis, namely segmentation, feature description, selection and extraction, and classification, are studied. As part of the segmentation process, the separation of clusters of partially occluded chromosomes, which is the critical stage that state-of-the-art chr...

The application of neural networks (NN’s) to automatic analysis of chromosome images is investigated in this paper. All aspects of the analysis, namely segmentation, feature description, selection and extraction, and classification, are studied. As part of the segmentation process, the separation of clusters of partially occluded chromosomes, which is the critical stage that state-of-the-art ch...

An Intrusion Detection System (IDS) monitors the behavior of a given environment and identifies the activities are malicious (intrusive) or legitimate (normal) based on features obtained from the network traffic data. In the proposed method, instead of considering all features for intrusion detection and wasting up the time in analyzing it, only the relevant feature for the particular attack is...