This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, ...

This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, ...

The present study applies two different machine learning (ML) algorithms to predict the stress-strain mapping for non-linear behaviour of thermoplastic materials: a Long Short-Term Memory (LSTM) algorithm and Feed-Forward Neural Network (FFNN). approach this work requires generation curve specific material parameters. training data are obtained from von Mises law Ramberg-Osgood equation. four c...

an artificial neural network has been used to determine the volume flux and rejections of ca2+ , na+ and cl¯, as a function of transmembrane pressure and concentrations of ca2+, polyethyleneimine, and polyacrylic acid in water softening by nanofiltration process in presence of polyelectrolytes. the feed-forward multi-layer perceptron artificial neural network including an eight-neuron hidden la...

A vector matrix real time backpropagation algorithm for recurrent neural networks that approximate multi-valued periodic functions," Received Unlike feedforward neural networks (FFNN) which can act as universal function ap-proximators, recursive, or recurrent, neural networks can act as universal approximators for multi-valued functions. In this paper, a real time recursive backpropagation (RTR...

We present in this paper, Fourier descriptor and feedforward neural network for face recognition. Analysis is done for various numbers of iterations. Comparison shows that faces are recognized with FFNN more accurately with 50000 iterations. For experiment, FERET database is used.

In this paper, several neural network and statistical learning approaches are proposed that learn to make human like decisions for the job assignment problem of the US Navy. Comparison study of Feedforward Neural Networks (FFNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Adaptive Bayes (AB) classifier with Generalized Estimation Equation (GEE) is provided. ...

Neural networks (NN) have demonstrated to be useful for estimating software development effort. A NN can be classified depending of its architecture. A Feedforward neural network (FFNN) and a General Regression Neural Network (GRNN) have two kinds of architectures. A FFNN uses randomization to be trained, whereas a GRNN uses a spread parameter to the same goal. Randomization as well as the spre...

Abstract Hybrid engines are becoming more and widespread. Electric energy instead is a valid help to reduce the environmental impact. In hybrid engines, number of components higher this results in decrease reliability. With Engine Health Monitoring (EHM) we mean set techniques used monitor health status system based on values assumed by some related parameters. Artificial Intelligence (AI) meth...