For continuous monitoring of river water quality , this study assesses the potential of using artificial neural networks (ANNs) for modeling the event-based suspended sediments concentration (SSC) in Jiasian diversion weir in southern Taiwan. The hourly data collected include the water discharge, turbidity and SSC during the storm events. The feed forward backpropagation network (BP), generaliz...

This paper presents a new model for predicting the compressive strength of steel-confined concrete on circular concrete filled steel tube (CCFST) stub columns under axial loading condition based on Artificial Neural Networks (ANNs) by using a large wide of experimental investigations. The input parameters were selected based on past studies such as outer diameter of column, compressive strength...

Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-Error-Propaga...

This paper presents an overview on applications of artificial neural network in electric power industry (EPI) which is currently undergoing an extraordinary development. One of the most thrilling and potentially cost-effective recent developments in this field is increasing usage of artificial intelligence techniques viz. artificial neural networks (ANNs), genetic algorithm, fuzzy logic, and ex...

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do no...

Neural networks are an artificial intelligence method for modeling complex non-linear functions. Artificial Neural Networks (ANNs) have been widely applied to the domain of prediction problems. Considerable research effort has gone into ANNs for modeling financial time series. This paper attempts to provide an overview of recent research in this area, emphasizing the issues that are particularl...

This study presents a new procedure based on Artificial Neural Network (ANN) for identification of double cracks in Functionally Graded Beams (FGBs). A cantilever beam is modeled using Finite Element Method (FEM) for analyzing a double-cracked FGB and evaluation of its first four natural frequencies for different cracks depths and locations. The obtained FEM results are verified against availab...

Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable to misdirection. Small amounts of noise can significantly affect their ability to correctly complete a task. Instead of generalizing concepts, ANNs seem to focus on surface statistical regularities in a given task. Here we compare how recurrent artificial neural networks, long short-term memory unit...

Artificial Neural Networks (ANNs) attempt to mimic human neural networks in order to perform tasks. In order to do this, tasks need to be represented in ways that the network understands. In ANNs these representations are often arbitrary, whereas in humans it seems that these representations are often meaningful. This article shows how using more meaningful representations in ANNs can be very b...

The objective of this study was to evaluate the efficiency of artificial neural networks (ANNs) for predicting genetic value in experiments carried out in randomized blocks. Sixteen scenarios were simulated with different values of heritability (10, 20, 30, and 40%), coefficient of variation (5 and 10%), and the number of genotypes per block (150 and 200 for validation, and 5000 for neural netw...