The use of backpropagation for training artificial neural networks (ANNs) is usually associated with a long training process. The user needs to experiment with a number of network architectures; with larger networks, more computational cost in terms of training time is required. The objective of this letter is to present an optimization algorithm, comprising a multiobjective evolutionary algori...

Scour in the downstream of hydraulic structures is a phenomenon which usually occurs due to exceeding the velocity or shear stress from a critical level. In this paper by using the laboratory data by Borman- Jouline and De-Agostino research, it was tried to get more accurate equations in order to calculate the maximum depth of scour in the downstream of the water level regulation structures. Co...

For Artificial Neural Networks (ANNs) to be effective modelling tools, they must draw upon biological characteristics: One characteristic often overlooked in the design of ANNs is the replication, or redundancy, of processes within the brain. This paper examines the effects of redundancy on the performance of ANNs trained on either a pattern classification task (e.g. parity, encoder) or a funct...

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...

Objective (s): Artificial Neural Networks (ANN) are widely used for predicting systems’ behavior. GMDH is a type of ANNs which has remarkable ability in pattern recognition. The aim the current study is proposing a model to predict dynamic viscosity of silver/water nanofluid which can be used as antimicrobial fluid in several medical purposes.Materials and Methods: In order to have precise mode...

Artificial Neural Networks (ANNs) were used to predict nanoparticle size and micropore surface area of polylactic acid nanoparticles, prepared by a double emulsion method. Different batches were prepared while varying polymer and surfactant concentration, as well as homogenization pressure. Two commercial ANNs programs were evaluated: Neuroshell Predictor, a black-box software adopting both neu...

Neural Networks are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex realworld sensor data, Artificial Neural Networks (ANNs) are among the most effective learning methods currently know. During the last decade they have been widely applied to the domain of financial time series prediction and their imp...

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. ...

objective(s): chitosan, a biodegradable and cationic polysaccharide with increasing applications in biomedicine, possesses many advantages including mucoadhesivity, biocompatibility, and low-immunogenicity. the aim of this study, was investigating the influence of ph, ratio of azelaic acid/chitosan and molecular weight of chitosan on loading efficiency of azelaic acid in chitosan particles. mat...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...