A neuro-fuzzy modeling tool (ANFIS) has been used to dynamically model cross flow ultrafiltration of milk. It aims to predict permeate flux and total hydraulic resistance as a function of transmembrane pressure, pH, temperature, fat, molecular weight cut off, and processing time. Dynamic modeling of ultrafiltration performance of colloidal systems (such as milk) is very important for design...

The paper presents an intelligent hybrid approach for content-based image retrieval based on texture feature. The proposed approach employs an Auto–Associative Neural Network (AANN) for feature extraction and a Multi–Layer Perceptron (MLP) with a single hidden layer for the classification. Two intelligent approaches such as AANN–MLP and statistical–MLP were investigated. The performance of the ...

In this study, we propose Multi-Layer Perceptron (MLP) with pulse glial chain including neurogenesis. In this network, we one-by-one connect glia unit with neurons in a hidden-layer. The glia generates a pulse according to the connecting neuron output. This pulse increases the threshold of the neuron and excites the neighboring glias. The pulse generation frequency is also changed according to ...

This paper presents a part of speech (POS) neuro tagger which consists of a 3-layer perceptron with elastic input. Computer experiments show that the neuro tagger has an accuracy of 94.4% for tagging ambiguous words when a small Thai corpus with 22,311 ambiguous words is used for training. A series of comparative experiments further show that the neuro tagger is de nitely far superior to the st...

In this paper, Rough Sets approach has been used to reduce the number of inputs for two neural networks-based applications that are, diagnosing plant diseases and intrusion detection. After the reduction process, and as a result of decreasing the complexity of the classifiers, the results obtained using Multi-Layer Perceptron (MLP) revealed a great deal of classification accuracy without affect...

Procedures using Artificial Neural Networks (ANN) are developed for characterizing square microstrip antennas. A Multi-Layer Perceptron (MLP) is used to find out the resonant frequency of the antennas. Same ANN is used to accomplish the task of obtaining different important antenna characteristics like gain, return loss and bandwidth of operation at once. The developed ANN is tested experimenta...

This paper proposes a new neuro-rough model for modelling the risk of HIV from demographic data. The model is formulated using Bayesian framework and trained using Markov Chain Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62% as opposed to 58% obtained from a Bayesian ...

The data model of independent component analysis (ICA) gives a multivariate probability density that describes many kinds of sensory data better than classical models like Gaussian densities or Gaussian mixtures. When only a subset of the random variables is observed, ICA can be used for regression, i.e. to predict the missing observations. In this paper, we show that the resulting regression i...

We propose a two-stage training for the multilayer perceptron (MLP). The first stage is bottom-up, where we use a class separability measure to conduct hidden layer training and the least squared error criterion to train the output layer. The second stage is top-down, we use a criterion derived from classification error rate to further train the network weights. We demonstrate the effectiveness...

We present a neuro-fuzzy system for character recognition from printed documents using a fuzzy Hough transform technique. For each character pattern, fuzzy Hough transform extracts information from the standard Hough transform accumulator cells into a number of fuzzy sets. These basic fuzzy sets are combined by t-norms to synthesize additional fuzzy sets whose heights form an n-dimensional feat...