Forecasting ragweed pollen concentration is a useful tool for sensitive people in order to prepare in time for high pollen episodes. The aim of the study is to use methods of Computational Intelligence (CI) (Multi-Layer Perceptron, M5P, REPTree, DecisionStump and MLPRegressor) for predicting daily values of Ambrosia pollen concentrations and alarm levels for 1-7 days ahead for Szeged (Hungary) ...

This work explores the Multi-layer Perceptron’s inference capabilities to detect textured relationships of pixels belonging to a squared neighbourhood. Although hidden in the neuron connections, these relationships lend the neural network the necessary discriminant power to classify patterns. Results similar to those involving the combination co-occurrence matrices-MLP have been obtained for su...

We study in this paper several methods that allow one to use interval data as inputs for Multi-layer Perceptrons. We show that interesting results can be obtained by using together two methods: the extremal values method which is based on a complete description of intervals, and the simulation method which is based on a probabilistic understanding of intervals. Both methods can be easily implem...

Although the multi-layer perceptron (MLP) neural networks provide a lot of flexibility and have proven useful reliable in wide range classification regression problems, they still limitations. One most common is associated with optimization algorithm used to train them. The commonly training method stochastic gradient descent backpropagation (or for short) because it mathematically tractable (g...

NETtalk is a classic example of a back-propagation trained multi-layer perceptron network applied to a practical application. NETtalk, created by Sejnowski and Rosen-berg 1], applies a multi-layer network to the text-to-speech problem. The goal is to develop a system which can convert English text into its underlying sequence of phonemes and stress markers. The string of phonemes and stress mar...

In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides capabilities of MLP NNs, it uses Back Propagation (BP) Gradient Descent (GD) training; therefore, NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well low-con...

In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...