This study shows the usefulness of Artificial Neural Network (ANN) in maintenance planning and man-agement. An ANN model based on the multi-layer perceptron having three hidden layers and four processing elements per layer was built to predict the expected downtime resulting from a breakdown or a maintenance activity. The model achieved an accuracy of over 70% in predicting the expected downtime.

the use of neural networks methodology is not as common in the investigation and pre-diction noise as statistical analysis. the application of artificial neural networks for pre-diction of power tiller noise is set out in the present paper. the sound pressure signals for noise analysis were obtained in a field experiment using a 13-hp power tiller. during measurement and recording of the sound ...

Consider a multilayer perceptron (MLP) with d inputs, a single hidden sigmoidal layer and a linear output. By adding an additional d inputs to the network with values set to the square of the rst d inputs, properties reminiscent of higher-order neural networks and radial basis function networks (RBFN) are added to the architecture with little added expense in terms of weight requirements. Of pa...

Limitations of one-hidden-layer perceptron networks to represent efficiently finite mappings is investigated. It is shown that almost any uniformly randomly chosen mapping on a sufficiently large finite domain cannot be tractably represented by a one-hidden-layer perceptron network. This existential probabilistic result is complemented by a concrete example of a class of functions constructed u...

This paper proposes a new method for prediction of chaotic time series based on Parallel Multi-Layer Perceptron (PMLP) net and dynamics reconstruction technique. The PMLP contains a number of multi-layer perceptron (MLP) subnets connected in parallel. Each MLP subnet predicts the future data independently with a different embedding dimension. The PMLP determines the final predicted result accor...

In solving classification task of data mining, the traditional algorithm such as multi-layer perceptron takes longer time to optimize the weight vectors. At the same time, the complexity of the network increases as the number of layers increases. In this study, we have used Functional Link Artificial Neural Networks (FLANN) for the task of classification. In contrast to multiple layer networks,...

In recent years, many machine learning methods have been used in network traffic identification.In order to improve the accuracy and solve some problems of network traffic identification, this paper presents a multi layer perceptron neural network-based method for network traffic identification, and parameters of multi-layer perceptron neural network are analyzed. Experimental results show that...

| We show that the Fourier transform of the linear output of a single hidden layer perceptron consists of a multitude of line masses passing through the origin. Each line corresponds to one of the hidden neurons and its slope is determined by that neuron's weight vector. We also show that convolving the output of the network with a function can be achieved simply by modifying the shape of the s...

Tree height is an important parameter for calculating forest carbon sink and assessing cycle. In order to obtain tree over a large area both efficiently at low cost, this study proposed Interferometric Synthetic Aperture Radar (InSAR) combined with machine learning method estimate the canopy height. The in was obtained using Unmanned Aerial Vehicle (UAV) photogrammetry, which considered be true...

Multi layer perceptron with back propagation algorithm is popular and more used than other neural network types in various fields of investigation as a non-linear predictor. Though MLP can solve complex and non-linear problems, it cannot use missing data for training directly. We propose a training algorithm with incomplete pattern data using conventional MLP network. Focusing on the fact that ...