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. 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 coefficients of...

Artificial neural networks (ANNs) are a new type of information processing system based on modeling the neural system of human brain. The prediction of swell pressures from easily determined soil properties, namely, initial dry density, initial water content, and plasticity index, have been investigated by using artificial neural networks. The results of the constant volume swell tests in oedom...

Enzymatic hydrolysis of cellulose is a complex process because of a number of inhibition and enzyme inactivation reactions which happen during hydrolysis. Artificial Neural Networks (ANNs) are very effective in developing predictive models for processes involving complex reaction kinetics that would otherwise be difficult to be modeled by more traditional deterministic approaches. The present i...

This paper presents the application of Fuzzy ARTMAP neural network for evaluating on-line load forecasting in short term case. A new approach using artificial neural networks (ANNs) is proposed for short term load forecasting. To forecast loads of a day, the hourly load pattern and the maximum and minimum and average of temprature must be determined. To demonstrate the effectiveness of the prop...

DOWNLOAD http://bit.ly/1OslRBc Artificial neural networks: theory and applications This comprehensive tutorial on artifical neural networks covers all the important neural network architectures as well as the most recent theory-e.g., pattern recognition, statistical theory, and other mathematical prerequisites. A broad range of applications is provided for each of the architectures. Artificial ...

in this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. experimental data was divided into two parts (70% for training and 30% for testing). optimal configuration of the network was obtained with minimization of prediction error on testing data. the accuracy of our proposed model was compared with four well-known empirical equations. the arti...

Artificial neural networks (ANNs) are computational models, loosely inspired by biological neural networks, consisting of interconnected groups of artificial neurons which process information using a connectionist approach. ANNs are widely applied to problems like pattern recognition, classification, and time series analysis. The success of an ANN application usually requires a high number of e...

In this paper, we propose a new hybrid model of artificial neural networks (ANNs) and genetic algorithm (GA) to optimal feature transformation and feature weighting. Previous research proposed several variants of hybrid ANNs and GA models including feature weighting, feature subset selection and network structure optimization. Among the vast majority of these studies, however, ANNs did not lear...

This paper presents the use of artificial neural network for the estimation of different performance parameters (i.e. Directivity, Radiation Efficiency, Gain and Bandwidth) of a coaxial feed equilateral triangular microstrip patch antenna. Levenberg-Marquardt training algorithms of MLPFFBP-ANN (Multilayer Perceptron feed forward back propagation Artificial Neural Network) has been used to imple...

The authors are presenting a thorough introduction in Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. The article covers a description of Artificial Neural Network methodology and points out the differences of Artificial Intelligence to traditional statistic models in terms of serving patients and clinicians, in a different way than current statistical anal...