Perceptron Back Propagation Neural Network Research Articles

LTR-MDTS structure - a structure for multiple dependent time series prediction

The subject of research presented in this paper is to model a neural network structure and appropriate training algorithm that is most suited for multiple dependent time series prediction / deduction. The basic idea is to take advantage of neural networks in solving the problem of prediction of synchronized basketball referees? movement during a basketball action. Presentation of time series stemming from the aforementioned problem, by using traditional Multilayered Perceptron neural networks (MLP), leads to a sort of paradox of backward time lapse effect that certain input and hidden layers nodes have on output nodes that correspond to previous moments in time. This paper describes conducted research and analysis of different methods of overcoming the presented problem. Presented paper is essentially split into two parts. First part gives insight on efforts that are put into training set configuration on standard Multi Layered Perceptron back propagation neural networks, in order to decrease backwards time lapse effects that certain input and hidden layers nodes have on output nodes. Second part of paper focuses on the results that a new neural network structure called LTR - MDTS provides. Foundation of LTR - MDTS design relies on a foundation on standard MLP neural networks with certain, left-to-right synapse removal to eliminate aforementioned backwards time lapse effect on the output nodes.

A Tutorial on Different Classification Techniques for Remotely Sensed Imagery Datasets

Classification techniques are used on large databases to develop models describing different data classes. Such analysis can provide deep insight for better understanding of different large-scale databases. Studies related to knowledge acquisition and effective knowledge development are also very popular in the remote sensing field with satellite imagery datasets. In any remote sensing research, the decision-making process mainly depends on the effectiveness of the classification process. Efficient classification techniques were developed and applied to the Statlog (Landsat Satellite) database at the University of California, Irvine Machine Learning Repository to identify six land type classes. We used three different classification algorithms on the large satellite imagery: multilayer perceptron backpropagation neural network (MLP BPNN), support vector machine (SVM), and k-nearest neighbor (k-NN). This research study aimed to evaluate the performance of these classification algorithms in the prediction of the classified lands from this large set of satellite imagery. We used different performance measures, such as classification accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and F-measure to evaluate the performance of each classifier. Among the three classification techniques applied, MLP BPNN turned out to be the best; next was k-NN, followed by SVM.

Neural network simulation of spring flow in karst environments

Daily discharges of two springs lying in a karstic environment were simulated for a period of 2.5 years with the use of a multi-layer perceptron back-propagation neural network. Two models were developed for the springs, one relying on the original data and another where the missing discharge values were supplemented by assuming linear relationships during base flow conditions. For both springs the mean square error of the two models did not differ significantly, with an improvement exhibited at the extremes, during the network’s training phase, by the model that utilized the extended data set, the results of which are reported here. The time lag between precipitation and spring discharge differed significantly for the two springs indicating that in karstic environments hydraulic behavior is dominated, even within a few hundred meters, by local conditions. Optimum training results were attained with a Levenberg–Marquardt algorithm resulting in a network architecture consisting of two input layer neurons, four hidden layer neurons, and one output layer neuron, the spring’s discharge. The neural network’s predictions captured the behavior for both springs and followed very closely the discontinuities in the discharge time series. Under-/over-estimation of observed discharges for the two springs remained below 3 %, with the exception of a few local maxima where the predicted discharges diverged more strongly from observed values. Inclusion of temperature data did not add to the improvement of predictions. Finally, optimum predictions were attained when past discharge data were added to the input record and discharge differentials rather than direct discharges were calculated resulting in elimination of any local maximum discrepancy between observed and predicted discharge values.

Knowledge-based evolving connectionist systems for condition evaluation of sustainable roadways: a feasibility study

In place, sustainable rehabilitation of existing deteriorated concrete highways through rubblization is considered to be a green and economical alternative to other options involving total reconstruction, etc. There is currently no standardised method of backcalculating the rubblized pavement layer moduli from pavement non-destructive test data through inverse analysis. This paper explores the feasibility of applying two evolving connectionist systems (ECOS), namely dynamic evolving neuro-fuzzy inference system (DENFIS) and evolving fuzzy neural network (EFuNN), to rubblized pavement moduli backcalculation. It is advantageous to employ ECOS networks for analysing complex engineered systems such as rubblized pavements since ECOS networks are resistant to catastrophic forgetting, have the ability to adapt to and learn new data as soon as they become available, do not have a limit to the amount of knowledge they can store and learn the examples very quickly compared to traditional multi-layered perceptron backpropagation neural networks (MLP-BP NN).

Neural Networks for Multiuser Detection of Signals in DS/CDMA Systems

Multiple access interference and near-far effect cause the performance of the conventional single user detector in DS/CDMA systems to degrade. Due to the high complexity of the optimum multiuser detector, suboptimal multiuser detectors with less complexity and reasonable performance have received considerable attention. In this paper, we analyse the performance of the multilayer perceptron backpropagation neural network as a multiuser detector of CDMA signals in AWGN and multipath fading channels. Our results show significant improvement over previous research. We compare neural network performance with the other detectors, and apply different neural networks and criteria, such as decision-based, fuzzy decision, discriminative learning, minimum classification, and cross entropy neural networks, and compare their performance. We propose a modified decision-based network which significantly improves the performance.

Neural network system for automated highway pavement inspection : Guralnick, S.A.; Suen, E.S.; Gu, J. Infrastructure Planning and Management Conference Proceedings, Denver, Colorado (United States), 21–23 Jun. 1993. pp. 272–276. Edited by J.L. Gifford, D.R. Vzarski and S. McNeil. American Society of Civil Engineers (1993). ISBN 0-87262-917-1

A multi-layer perceptron back propagation neural network based methodology has been developed to process and analyze moire fringes obtained with an automated highway pavement inspection system developed in our laboratory. The sensitivity, accuracy, and efficiency of the model is compared with benchmark results obtained by means of traditional techniques to evaluate the overall system performance.