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Fundamentals of neural networks
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The article was published on 1993-01-01 and is currently open access. It has received 1921 citations till now. The article focuses on the topics: Time delay neural network & Physical neural network.read more
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Journal ArticleDOI
Using artificial neural networks and self-organizing maps for detection of airframe icing
TL;DR: A method of using Artificial Neural Networks and Kohonen Self-Organizing Maps to detect ice on a horizontal tail is proposed and investigated and the system is shown to be capable of acting in an advisory role for the flight crew.
Journal ArticleDOI
Improving flood forecasting in Bangladesh using an artificial neural network
TL;DR: In this paper, the root mean square and mean absolute error were found to be between 0.537 and 0.154m for up to ten days with very high accuracy for real-time flood forecasting.
Journal Article
A Novel Mobile Robot Navigation System Using Neuro-Fuzzy Rule-Based Optimization Technique
TL;DR: A new novel approach to control the autonomous mobile robot that moved along a collision free trajectory until it reaches its target is proposed in this study using a hybrid neuro-fuzzy method where the neural network effectively chooses the optimum number of activation rules in order to reduce computational time for real-time applications.
Journal ArticleDOI
Application of an artificial neural network to improve short-term road ice forecasts
TL;DR: How a three-layer artificial neural network (NN) can be used to improve the accuracy of short-term automatic numerical prediction of road surface temperature in order to cut winter road maintenance costs, reduce environmental damage from oversalting and provide safer roads for road users is described.
Dissertation
Seismic Retrofit Cost Modelling of Existing Structures
TL;DR: In this paper, the authors used the backward elimination (BE) regression technique to properly explore the extent of the influence of independent variables on the retrofit net construction cost, and consequently those variables that made a statistically significant contribution to the prediction of the RNCC were identified.