The idea of using Feed-Forward Neural Networks (FFNNs) as regression functions for Nonlinear AutoRegressive eXogenous (NARX) models, leading to models herein named NARXs (NNARXs), has been quite popular in the early days machine learning applied nonlinear system identification, owing their simple structure and ease application control design. Nonetheless, few theoretical results are available c...

In this paper, critical conditions in electric power systems are monitored by applying various neural networks. In order to accomplish the stated goal, the authors tried several combinations of Feed Forward Neural Network and Layer Recurrent Neural Networks by imparting appropriate training schemes through supervised learning in order to formulate a comparative analysis on their performance. On...

ABSTRACT The constant monitoring of water quality is fundamental for the understanding aquatic environment, yet it demands great financial investments and susceptible to inconsistencies missing values. Using a database composed 59 sampling campaigns, performed 12 years, on 10 stations along Iguassu River Basin (Southern Brazil), this study presents model, based feed-forward neural networks, whi...

it is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. hence, at present study, two models including artificial neural networks and nonlinear multivariate regression were used to predict peak discharge in taleghan watershed. maximum daily mean discharge and corresponding daily rainfall, one day antecedent and...

Inspired by biological neuronal systems, artificial neural networks have demonstrated superior characteristics of learning, adaptation, classification and function-approximation. They have been successfully applied to many areas. However, conventional neural networks are feed-forward networks that move the information in only one direction, forward, from the inputs to the outputs. There are no ...

Complex Valued Neural Network is one of the open topics in the machine learning society. In this paper we will try to go through the problems of the complex valued neural networks gradients computations by combining the global and local optimization algorithms. The outcome of the current research is the combined global-local algorithm for training the complex valued feed forward neural network ...

This paper, presents a theoretical and practical basis of preprocessing on handwritten text for character recognition using forward-feed neural networks. Afterwards, the Feed forward algorithm gives working of a neural network followed by the Back Propagation Algorithm which compromises Training, Calculating Error, and Modifying Weights. The proposed solutions focus on applying Back Propagation...

Recent advancements in feed-forward convolutional neural network architecture have unlocked the ability to effectively use ultra-deep neural networks with hundreds of layers. However, with a couple exceptions, these advancements have mostly been confined to the world of feed-forward convolutional neural networks for image recognition, and NLP tasks requiring recurrent networks have largely been...

The estimation and forecasting of the hydrologic data carry significance for many water resources engineering problems. Establishing sediment monitoring instruments on rivers is a costly operation. The methods available in literature for sediment concentration estimation are complicated, time consuming and necessitate cumbersome parameter estimation procedures. Artificial neural networks have b...