This paper uses Multi-Layer Perceptron Neural Network (MLPNN) for comparing the linear dimensionality reduction techniques (DRTs) for fault diagnosis in rolling element bearing (REB).The vibration signals from normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired under different radial loads and ...

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of ophthalmic arteries. In this study, ophthalmic arterial Doppler signals were obtained from 106 subjects, 54 of whom suffered from ocular Behcet disease while the rest were healthy subjects. Multilayer perceptron neural network (MLPNN) employing delta-bar-delta training algorithm wa...

The new method presented in this study was directly based on the consideration that internal carotid arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for detecting variabilities such a...

In this study, EEG signals were classified by using the average powers extracted by means of the rectangle approximation window based average power method from the power spectral densities of frequency sub-bands of the signals and two different artificial neural networks (ANNs) which are adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLPNN). In order to ...

In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using multi-boosting technique and MLPNN. The model tested in Amol City, Iran, data-scarce city an ungauged area which is prone to severe inundation events currently lacks prevention infrastructure. Performance of the compared with that standalone MLPNN model, ran...

Equalization of satellite communication using complex-bilinear recurrent neural network (C-BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used in modeling highly nonlinear systems with time-series characteristics more effectively than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C-BLRNN) for dea...

392 Abstract— All bank marketing campaigns are dependent on customers’ huge electronic data. The size of these data source is impossible for a human analyst to come up with interesting information that will help in the decision-making process. Data mining models are completely helping in performance of these campaigns. This paper introduces applications of recent and important models of data mi...

The new method presented in this study was directly based on the consideration that ophthalmic arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for determining variabilities such as st...

The implementation of corrosion detection in submarine pipelines is difficult, and a combined PCA-MLP prediction model proposed to improve the accuracy pipelines. Firstly, rate multiphase flow pipeline South China Sea simulated by De Waard 95 transient simulation software OLGA compared with actual rate; then, according data OLGA, principal component analysis (PCA) used reduce dimensionality fac...