Using a standard laboratoty medical test, --serum electrophoresis--, several pathologic conditions, such as liver cirrhosis or nephrotic syndrome can be diagnosed. In all of these cases, the presence (in excess) or absence of certain types of proteins can be ascertained. This paper explains the status of the developement of an artificial neural net-based system that will help to diagnose these ...

For many applications, to reduce the processing time and the cost of decision making, we need to reduce the number of sensors, where each sensor produces a set of features. This sensor selection problem is a generalized feature selection problem. Here, we first present a sensor (group-feature) selection scheme based on Multi-Layered Perceptron Networks. This scheme sometimes selects redundant g...

In the urban areas, public transport service interacts with the private mobility. Moreover, on each link of the urban public transport network, the bus speed is affected by a high variability over time. It depends on the congestion level and the presence of bus way or no. The scheduling reliability of the public transport service is crucial to increase attractiveness against private car use. A ...

One of the main problems in chaotic time series prediction is that the underlying nonlinear dynamics is usually unknown. Using a nonlinear predictor to predict a chaotic time series usually puts a limit on the accuracy since the nonlinear predictor is basically an approximation of the unknown nonlinear mapping. In this paper, we propose using fusion of predictors as a method to improve the perf...

The focus of the automatic solar flare detection is on the development of efficient feature-based classifiers. The three principal techniques used in this work are Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM) classifiers. We have experimented and compared these three methods for solar flare detection on the solar Hα (Hydrogen-Alpha) images obtained...

Neural network process modelling needs the use of experimental design and studies. A new neural network constructive algorithm is proposed. Moreover, the paper deals with the influence of the parameters of radial basis function neural networks and multilayer perceptrons network in process modelling. Particularly, it is shown that the neural modelling, depending on learning approach, cannot be a...

In this article an attempt is made to study the applicability of a general purpose, supervised feed forward neural network with one hidden layer, namely. Radial Basis Function (RBF) neural network. It uses relatively smaller number of locally tuned units and is adaptive in nature. RBFs are suitable for pattern recognition and classification. Performance of the RBF neural network was also compar...

An experimental investigation of the cascade-correlation network (CC) is carried out in diierent benchmarking pattern recognition problems. An extensive experimental framework is developed to establish a comparison between the CC network and the more traditional multilayer perceptron (MLP) and radial basis function models (RBF). The diierent networks are evaluated with respect to generalization...

Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improve...

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...