With computers to handle more and more complicated things in variable environments, it becomes an urgent requirement that the artificial intelligence has the ability of automatic judging and deciding according to numerous specific conditions so as to deal with the complicated and variable cases. ANNs inspired by brain is a good candidate. However, most of current numeric ANNs are not good at re...

artificial neural networks (anns) are computer techniques that attempt to simulate the functionality and decision-making processes of the human brain. in the past few decades, artificial neural networks (anns) have been extensively used in a wide range of engineering applications. there are only a few applications in liquid membrane process. the objective of this research was to develop artific...

Artificial neural networks (ANNs) are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and the choice of a learning algorithm have to be addressed. Evolutionary search methods can provide an automatic solution to these probl...

In manufacturing processes, maintaining quality is associated with proper process mean parameters and product quality metrics. The early detection of mean changes is important to reduce the number of defectives or non-conformities in the production. In this work, a diverse ensemble of Artificial Neural Networks (ANNs) with a leader network have been used to achieve this purpose, then a performa...

Nowadays, estimating the ampere consumption and achieve to the optimum condition from the perspective of energy consumption is one of the most important steps to reduce the production costs. In this research it is tried to develop an accurate model for estimating the ampere consumption by using the artificial neural networks (ANN).In the first step, experimental studies were carried out on 7 ca...

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...

This paper describes the implementation of multivariate data analysis: NEURODOC applies the axial k-means method for automatic, non-hierarchical cluster analysis and a Principal Component Analysis (PCA) for representing the clusters on a map. We next introduce Artificial Neural Networks (ANNs) to extend NEURODOC into a neural platform for the cluster analysis and cartography of bibliographic da...

Application of Artificial Neural Networks (ANNs) for electrical load forecasting has been proposed in the literature. ANNs have some inherent drawbacks and limitations, such as difficulty in deciding the structure of ANN, selection of type of neuron, large training time, sticking to local minima, etc. To overcome the drawbacks of ANN, a Generalized Neural Network (GNN) has been proposed in the ...

In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears and bearings. DWT is an advanced signal-processing technique for fault detection and identification. Five features of wavelet transform RMS, crest factor, kurtosis, standard deviation and skewness of discrete wavelet co...

One biological principle that is often overlooked in the design of artificial neural networks (ANNs) is redundancy. Redundancy is the replication of processes within the brain. This paper examines the effects of redundancy on learning in ANNs when given either a function-approximation task or a pattern-classification task. The function-approximation task simulated a robotic arm reaching toward ...