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.

We study the first-order phase transition in the model of a simple perceptron with continuous weights and large, but finite value of the inputs. Making the analogy with the usual finite-size physical systems, we calculate the shift and the rounding exponents near the transition point. In the case of a general perceptron with larger variety of outputs, the analysis gives only bounds for the expo...

The error backpropagation (EBP) algorithm for training feedforward multilayer perceptron (FMLP) has been used in many applications because it is simple and easy to implement. However, its gradient descent method prevents EBP algorithm from converging fast. To overcome the slow convergence of EBP algorithm, the second order methods have adapted. Levenberg-Marquardt (LM) algorithm is estimated to...

Since artificial neural networks (ANNs) can approximate any function, they have been applied in many fields including hydrology. In hydrology, there are important issues such as flood estimation and predicting rainfall-runoff in a certain area. In this presentation, we briefly introduce a popular feed-forward neural network model, so called “multi-layer perceptron (MLP)”, and review its applica...

A method is described for designing speaker recognition features that are robust to telephone handset distortion. The approach transforms features such as mel-cepstral features, log spectrum, and prosody-based features with a non-linear arti®cial neural network. The neural network is discriminatively trained to maximize speaker recognition performance speci®cally in the setting of telephone han...

The carbonation tower is a key reactor to manufacturing synthetic soda ash using the Solvay process. Because of the complexity of the reaction in the tower, it is difficult to control such a nonlinear large-time-delay system with normal measurement instrumentation. To solve this problem, a time-delay neural network (TDNN) is used in the soft measurement model in this paper. A special back-propa...

Abs t rac t : W e propose an architecture of a multilayer quadratic perceptron (MLQP) that combines advantages of multilayer perceptrons(MLPs) and higher-order feedforward neural networks. The features of MLQP are in its simple structure, practical number of adjustable connection weights and powerful learning ability. I n this paper, the architecture of MLQP is described, a backpropagation lear...

Software engineering cost models and estimation techniques are used for number of purposes. These include budgeting, tradeoff and risk analysis, project planning and control, software improvement and investment analysis. The proposed work uses neural network based estimation, which is essentially a machine learning approach, is one of the most popular techniques. In this paper the author has pr...

Whitened Neural Network (WNN) is a recent advanced deep architecture, which improves convergence and generalization of canonical neural networks by whitening their internal hidden representation. However, the whitening transformation increases computation time. Unlike WNN that reduced runtime by performing whitening every thousand iterations, which degenerates convergence due to the ill conditi...

The use of the neural networks to estimate software development effort has been viewed with skepticism by the majority of the cost estimation community. Although, neural networks have shown their strengths in solving complex problems, their shortcoming of being ‘black boxes’ has prevented them to be accepted as a common practice for cost estimation. In this paper, we study the interpretation of...