Artificial Neural Network (ANN) is a new data mining technique that is finding applications in a number of areas. ANN is inspired from the biological nervous system. We propose through this paper a new application of ANN which is the octree generation. An octree is a tree data structure in which each internal node has exactly eight children. Octrees are most often used to partition a three dime...

Forecasting accuracy drives the performance of inventory management. This study is to investigate and compare different forecasting methods like Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) with Neural Networks (NN) models as Feed-forward NN and Nonlinear Autoregressive network with eXogenous inputs (NARX). Data used to forecast is acquired from inventory database of...

In Neural Networks (NN), Adaptive Activation Functions (AAF) have parameters that control the shapes of activation functions. These parameters are trained along with other parameters in the NN. AAFs have improved performance of Neural Networks (NN) in multiple classification tasks. In this paper, we propose and apply AAFs on feedforward NNs for regression tasks. We argue that applying AAFs in t...

We use Neural Networks (NN) in order to design control architectures for autonomous mobile robots. With PVM, it is possible to spawn different parts of a NN on different workstations. Specific message passing functions using PVM may be included into the NN architecture. A graphical interface helps the user spawning the NN architecture, and monitors the messages exchanged between the different s...

This paper aims to illustrate a novel approach of complex disease prediction, exemplified by a coronary artery disease (CAD) study that we have developed. This multidisciplinary approach straddles fields of microarray technology and genetics, neural networks (NN), data mining and machine learning, as well as traditional statistical analysis techniques, namely principal components analysis (PCA)...

We chronicle the research on the fusion technology of neural networks and fuzzy systems (NN+FS), the models that have been proposed from this research, and the commercial products and industrial systems that have adopted these models. First, we review the NN+FS research activity during the early stages in Japan, the US, and Europe. Next, following the classification of NN+FS models, we show the...

This paper proposes two hybrid connectionist structural acoustical models for robust context independent phone like and word like units for speaker-independent recognition system. Such structure combines strength of Hidden Markov Models (HMM) in modeling stochastic sequences and the non-linear classification capability of Artificial Neural Networks (ANN). Two kinds of Neural Networks (NN) are i...

An optimal choice of proper kinematical variables is one of the main steps in using neural networks (NN) in high energy physics. Our method of the variable selection is based on the analysis of a structure of Feynman diagrams (singularities and spin correlations) contributing to the signal and background processes. An application of this method to the Higgs boson search at the Tevatron leads to...

This article deals with hybrid expert system that has knowledge base realized through a hierarchical structure of artificial neural networks (NN). The decision tree is built by C4.5 algorithm at first. In the next step the nods of the tree are replaced by NN. They are trained to split the data in the same way as the nods. So the problem is separated into partial sub-problems that are solved by ...