In the artificial neural networks (ANNs), feature selection is a wellresearched problem, which can improve the network performance and speed up the training of the network. The statistical-based methods and the artificial intelligence-based methods have been widely used to feature selection, and the latter are more attractive. In this paper, using genetic algorithm (GA) combining with mutual in...

Neural networks are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex real-world sensor data, artificial neural networks (ANNs) are among the most effective learning methods. During the last decade, they have been widely applied to the domain of financial time series prediction, and their importance in t...

through the last three decades different seismological and engineering approaches for the generation of artificial earthquakes have been proposed. selection of an appropriate method for the generation of applicable artificial earthquake accelerograms (aeas) has been a challenging subject in the time history analysis of the structures in the case of the absence of sufficient recorded accelerogra...

Recent developments in Artificial Neural Networks (ANNs) have opened up new possibilities in the domain of inverse problems. For inverse problems like structural identification of large structures (such as bridges) where in situ measured data are expected to be imprecise and often incomplete, ANNs may hold greater promise. This study presents a method for estimating the damage intensities of jo...

Artificial neural networks (ANNs) are model-free computationat tools dtat can "learn" dte linear or nonlinear rules embedded in a dataset. We report dte results of an attempt to utilize ANNs in dte field of reaction kinetics. A feedforward network is trained to predict dte main dynamical feamres of oligoasci/lations in dte acidic bromate-ascorbic acid-malonic acid reacting mixture, in which the...

Artificial neural networks (ANNs) are general function approximators and noise resistant, and therefore popular in many applications. Researchers in the field of computational intelligence have shown that biologically sound spiking neural networks (SNNs) are comparable, or even more powerful than traditional artificial neural networks(ANNs) [1]. However, such neural networks are usually computa...

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...

De Weijer, A.P., Lucasius, C.B., Buydens, L., Kateman, G. and Heuvel, H.M., 1993. Using genetic algorithms for an artificial neural network model inversion. Chemometrics and Intelligent Laboratory Systems, 20: 45-55. Genetic algorithms (GAs) and artificial neural networks (ANNs) are techniques for optimization and learning, respectively, which both have been adopted from nature. Their main adva...

Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and typically suffer from a number of shortcomings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing neural networks that use static and dynamic topo...