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 contribution, one and two-stage neural networks methods for face recognition are presented. For two-stage systems, the Kohonen self-organizing map is used as a feature extractor and multiplayer perceptron (MLP) or radial basis function (RBF) network are used as classifiers. The results of such recognition are compared with face recognition using a one-stage multilayer perceptron and rad...

alzheimer disease is one form of dementia in old age. alzheimer disease, the incurable disease, which is usually in the seventh decade of human life, shows its symptoms. the disease may be present for years without clinical symptoms. the current study identified the genes with altered expression in patients with alzheimer disease. the important sequence of each gene in alzheimer disease was fou...

This paper investigates the credit scoring accuracy of "ve neural network models: multilayer perceptron, mixture-of-experts, radial basis function, learning vector quantization, and fuzzy adaptive resonance. The neural network credit scoring models are tested using 10-fold crossvalidation with two real world data sets. Results are benchmarked against more traditional methods under consideration...

purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learni...

In our previous work we proposed a Multilayer Perceptron Neural Networks (MLP NN) consisting of fuzzy flipflops (F3) based on various operations. We showed that such kind of fuzzy-neural network had good learning properties. In this paper we propose an evolutionary approach for optimizing fuzzy flip-flop networks (FNN). Various popular fuzzy operation and three different fuzzy flip-flop types w...

The authors present results of the research aiming at human identification based on tensor representation of the gait motion capture data. High-dimensional tensor samples were reduced by means of the multilinear principal component analysis (MPCA). For the purpose of classification the following methods from the WEKA software were used: k Nearest Neighbors (kNN), Naive Bayes, Multilayer Percept...

This work analyzes the advantages of Artificial Neural Networks applied to sensorless control for Switched Reluctance Motors. Due to the non-linear electrical characteristics of Switched Reluctance Motors, Artificial Neural Networks perform a good tool and for torque ripple minimization in Switched Reluctance Motors. The simulation results to prove the efficiency of a multilayer perceptron in o...