The recycled aggregate is an alternative with great potential to replace the conventional concrete alongside other benefits such as minimising usage of natural resources in exploitation produce new concrete. Eventually, this will lead reducing construction waste, carbon footprints and energy consumption. This paper aims study compressive strength using Artificial Neural Network (ANN) which has ...

The hidden representation of a recurrent neural network language model (RNNLM) is regarded as a summary of the past input sequence. In this study, we propose that the hidden representation also consists of the expectation about upcoming inputs. A RNNLM is originally trained to predict the next word or character, but we experimentally discover, even for an unmodified RNNLM, the farther sequences...

Artificial neural networks, ANNs, have been widely used especially in the last two decades. ANNs have a wide range of scientific applications, they are used for pattern recognition and forecasting of natural mechanisms. In water treatment, ANNs have enormous potential, especially to support workers in plant operation. In clear water plants are taking large volumes of data, especially informatio...

Neural networks for implementing large networked systems such as smart electric power grids consist of multiple inputs and outputs. Many outputs lead to a greater number of parameters to be adapted. Each additional variable increases the dimensionality of the problem and hence learning becomes a challenge. Cellular computational networks (CCNs) are a class of sparsely connected dynamic recurren...

Artificial neural networks (ANNs) have become a very powerful tool in the approximation of high-dimensional functions. Especially, deep ANNs, consisting large number hidden layers, been successfully used series practical relevant computational problems involving input data ranging from classification tasks supervised learning to optimal decision reinforcement learning. There are also mathematic...

The paper is oriented to introduce different dimension reduction methods in the text document retrieval area. First, the mostly used text document retrieval models are described, and then in second part the analytical approach and neural network approaches to dimension reduction of keyword space are described. Dimension reduction methods reduce keyword space into much smaller size together with...

In this paper, we investigate the relation between Artificial Neural Networks (ANNs) and networks of populations of spiking neurons. The activity of an artificial neuron is usually interpreted as the firing rate of a neuron or neuron population. Using a model of the visual cortex, we will show that this interpretation runs into serious difficulties. We propose to interpret the activity of an ar...

this paper presents the application of three main artificial neural networks (anns) in damage detection of steel bridges. this method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. the changes in structural response is used to identify the states of structural damage. to circumvent the difficulty arising from the non-linear n...

Abstract The focus of this work is on the application classical Model Order Reduction techniques, such as Active Subspaces and Proper Orthogonal Decomposition, to Deep Neural Networks. We propose a generic methodology reduce number layers in pre-trained network by combining aforementioned techniques for dimensionality reduction with input-output mappings, Polynomial Chaos Expansion Feedforward ...