An enhanced time-delay neural network (TDNN), using time series sensor response data, improved pattern recognition ability of an electronic nose (e-nose) in discriminating four different spices. TDNN was used for analysis of e-nose time series sensor data from 0 to 4 min, while two popular pattern recognition methods, discriminant function analysis (DFA) and multilayer perceptron (MLP) trained ...

In this paper, results are presented from a comprehensive series of studies aimed at assessing the suitability of multilayered perceptron (MLP) and radial basis function (RBF) networks for use in embedded, microcontroller-based, condition monitoring and fault diagnosis (CMFD) applications. Our assessment criteria include the performance of each classifier on a range of CMFD-related problems, su...

MAXIMUM LIKELIHOOD ESTIMATION OF EXPONENTIALS CONTAINED IN SIGNAL-DEPENDENT NOISES Publication No._______ Steven John Apollo, Ph.D. The University of Texas at Arlington, 1991 Supervising Professor: Michael T. Manry The problem of maximum likelihood estimation (MLE) of exponentials in signal-dependent noise is addressed as well as a methodology to attack the problem. Estimation of exponentials h...

In our previous work we proposed a Multilayer Perceptron Neural Networks (MLP NN) consisting of fuzzy flipflops (F3) based on various operations. We showed that such kind of fuzzy-neural network had good learning properties. In this paper we propose an evolutionary approach for optimizing fuzzy flip-flop networks (FNN). Various popular fuzzy operation and three different fuzzy flip-flop types w...

This paper proposed ship classification based on covariance of discrete wavelet using probability Neural Network A set of ship profiles are used to build a covariance matrix by discrete wavelet transform using Neural Network. It is found that this method for ship classifier design offers good class discriminacy when trained with 5 ship classes. This method can discriminate noisy ship very well....

The learning process for multi layered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow-Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, this iterative formulas result in terms formed by the principal components of the weight matri...

Introduction: Artificial neural networks are a type of systems that use very complex technologies and non-algorithmic solutions for problem solving. These characteristics make them suitable for various medical applications. This study set out to investigate the application of artificial neural networks for differential diagnosis of thalassemia minor and iron-deficiency anemia. Methods: It is...

This paper investigates neural network based estimation of NOx emissions in a thermal power plant, fed with both oil and methane fuels. Two types of neural network namely a novel ‘eng-genes’ architecture and a Multilayer Perceptron (MLP) have been developed, both being optimised using genetic algorithms. Due to the local nature of the NOx generation process, operational information on the burne...

The response of a multilayered perceptron (MLP) network on points which are far away from the boundary of its training data is generally never reliable. Ideally a network should not respond to data points which lie far away from the boundary of its training data. We propose a new training scheme for MLPs as classifiers, which ensures this. Our training scheme involves training subnets for each ...

Streams of irregularly occurring events are commonly modeled as a marked temporal point process. Many real-world datasets such e-commerce transactions and electronic health records often involve where multiple event types co-occur, e.g. items purchased or diseases diagnosed simultaneously. In this paper, we tackle multi-label prediction in problem setting, propose novel Transformer-based Condit...