This paper presents a "direct" method to gas-oil interface level determination using an artificial neural network approach based on Electrical Capacitance Tomography (ECT) measurements. "Direct" here means that the gas-oil interface levels are obtained directly from the ECT measurements without recourse to image reconstruction. The preliminary work models a separation tank that i~ filled with g...

conclusions results showed that ann is a powerful tool for predicting sorption coefficients using soil organic carbon content variations. results the multilayer perceptron (mlp) artificial neural networks (ann) model with 1-6-1 layout, predicted nearly 98% of the variance of kd as well as 94% of the koc variations with soil organic carbon content. materials and methods data of this study were d...

We propose a reconfigurable neural network structure which has capability to process supervised or unsupervised learning algorithm computation. The proposed structure is based on modular structure which can configure artificial neural network architecture flexibly. Main processing unit of the proposed structure is designed to obtain flexibility of its internal structure by specific instructions...

A comparison of corporate failure models in Australia: Hybrid neural networks, logit models and discriminant analysis. A comparison of corporate failure models in Australia: Hybrid neural networks, logit models and discriminant analysis. Abstract This study investigated whether two artificial neural networks (ANNs), multilayer perceptron (MLP) and hybrid networks using statistical and ANN appro...

In this work, a channel estimation technique based on Artificial Neural Networks (ANN) has been proposed as an alternative to pilot based channel estimation technique for Space-Time Block Coded Multiple-Input Multiple-Output (STBCMIMO) systems over Rayleigh fading channels. ANNs, due to their high degree of adaptability, can be used for modelling the nonlinear phenomenon of channel estimation a...

Handwritten signatures are the most natural way of authenticating a person’s identity. An offline signature verification system generally consists of four components: data acquisition, preprocessing, feature extraction, recognition and verification. This paper presents a method for verifying handwritten signature by using NN architecture. In proposed methods the multi-layer perceptron (MLP), mo...

In this paper, two feed forward neural network models have been presented to predict the Silicon Modification Level (SiML) of W319 aluminum alloys using the Thermal Analysis (T.A) parameters as inputs. The developed neural networks are a Multilayer Perceptron (MLP) network and a Radial Basis Function (RBF) network. The neural network models were found to predict the SiML accurately (R=0.99). Th...

In this paper, learning algorithm for a single multiplicative spiking neuron (MSN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is found that a single MSN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Several benchmark and real-life ...

This paper proposes a new hybrid approach which combines simulated annealing and standard backpropagation for optimizing Multi Layer Perceptron Neural Networks for time series prediction. Experimental results have shown that this approach selects the appropriate time series lags and builds an MLP with adequate number of hidden neurons required for achieving good performance on the task. The per...

In this paper, a novel and effective criterion based on the estimation of the signal-to-noise-ratio figure (SNRF) is proposed to optimize the number of hidden neurons in neural networks to avoid overfitting in the function approximation. SNRF can quantitatively measure the useful information left unlearned so that overfitting can be automatically detected from the training error only without us...