In this study, an Artificial Neural Network (ANN) approach is utilized to perform a parametric study on the process of extraction of lubricants from heavy petroleum cuts. To train the model, we used field data collected from an industrial plant. Operational conditions of feed and solvent flow rate, Temperature of streams and mixing rate were considered as the input to the model, whereas the flo...

This paper presents methods which are aimed at finding approximations to missing data in a dataset by using optimization algorithms to optimize the network parameters after which prediction and classification tasks can be performed. The optimization methods that are considered are genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), random forest (RF) and negativ...

Meta-learning consists in learning learning algorithms. We use a Long Short Term Memory (LSTM) based network to learn to compute on-line updates of the parameters of another neural network. These parameters are stored in the cell state of the LSTM. Our framework allows to compare learned algorithms to hand-made algorithms within the traditional train and test methodology. In an experiment, we l...

in this paper, we introduce a takagi-sugeno (ts) fuzzy model which is derived from a typical multi-layer perceptron neural network (mlp nn). at first, it is shown that the considered mlp nn can be interpreted as a variety of ts fuzzy model. it is discussed that the utilized membership function (mf) in such ts fuzzy model, despite its flexible structure, has some major restrictions. after modify...

Software defect prediction has turned into an expected requirement for organizations to guarantee quality and reliability of software products. The early defect prediction can encourage managers to amend and improve reliability of product. Methodologies, for example, machine learning and neural network have ended up as eminent solution for training and classification of data and can be importan...

In original minority game, each agent at every time step chooses the highest-score strategy from its limited number of strategies given arbitrarily in the beginning of the game. In this paper, agents of the minority game are equipped to a multi-layer perceptron neural network that at every time step learns by back propagation error. Simulation results and the comparison of them with the results...

A Bayesian implementation of learning in neural networks using Monte Carlo sampling has been developed by Neal (1996). This computation intensive method has shown encouraging performance in (Neal 1996) and in a study using several datasets in (Rasmussen 1996). For a full description of the method the reader is referred to (Neal 1996). Here a brief description of the algorithm will be given, alo...

An iris pattern recognition system is developed for CASIA database using Singular Value Decomposition transform as a tool for feature extraction. Experimental prototype pattern recognition (PR) system is designed in which iris images of ten different persons are given as input to the system. After localizing region of interest (ROI), features are extracted with respect to image statistics, text...