The Multi-Layer Perceptron (MLP) is one of the most widely applied and researched Artificial Neural Network model. MLP networks are normally applied to performing supervised learning tasks, which involve iterative training methods to adjust the connection weights within the network. This is commonly formulated as a multivariate non-linear optimization problem over a very high-dimensional space ...

Reliable results are crucial when working with medical decision support systems. A decision support system should be reliable but also be interpretable, i.e. able to show how it has inferred its conclusions. In this thesis, the preprocessing perceptron is presented as a simple but effective and efficient analysis method to consider when creating medical decision support systems. The preprocessi...

Hyperspectral images have significant applications in various domains, since they register numerous semantic and spatial information the spectral band with variability of signatures. Two critical challenges identifying pixels hyperspectral image are respectively representing correlated among local global, as well abundant parameters model. To tackle this challenge, we propose a Multi-Scale U-sh...

A multilayer perceptron is a feed forward artificial neural network model that maps sets of input data onto a set of appropriate output. It is a modification of the standard linear perceptron in that it uses three or more layers of neurons (nodes) with nonlinear activation functions and is more powerful than the perceptron in that it can distinguish data that is not linearly separable, or separ...

The inverse of the Fisher information matrix is used in the natural gradient descent algorithm to train single-layer and multi-layer perceptrons. We have discovered a new scheme to represent the Fisher information matrix of a stochastic multi-layer perceptron. Based on this scheme, we have designed an algorithm to compute the natural gradient. When the input dimension n is much larger than the ...

The automatic vehicle classification system has emerged as an important field of study in image processing and machine vision technologies’ implementation because of its variety of applications. Despite many alternative solutions for the classification issue, the vision-based approaches remain the dominant solutions due to their ability to provide a larger number of parameters than other approa...

In this paper, we propose a new method of phoneme segmentation using MLP(multi-layer perceptron). The structure of the proposed segmenter consists of three parts: preprocessor, MLP-based phoneme segmenter, and postprocessor. The preprocessor utilizes a sequence of 44 order feature parameters for each frame of speech, based on the acoustic-phonetic knowledge. The MLP has one hidden layer and an ...

In this paper, a multilayer perceptron guided encryption/decryption (STMLP) in wireless communication has been proposed for exchange of data/information. Multilayer perceptron transmitting systems at both ends generate an identical output bit and the network are trained based on the output which is used to synchronize the network at both ends and thus forms a secret-key at end of synchronizatio...

This paper presents a multi-layer perceptron model for the estimation of classrooms number of occupants from sensed indoor environmental data–relative humidity, air temperature, and carbon dioxide concentration. The modelling datasets were collected from two classrooms in the Secondary School of Pombal, Portugal. The number of occupants and occupation periods were obtained from class attendance...

Previous approaches to texture analysis and segmentation pe$orm multi-channel filtering by applying a set of filters in frequency domain or a set of masks in spatialdomain. In this paper we describe a texture segmentation algorithm based on multi-channel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been appli...