This paper presents an application of Multi-layer Perceptrons (MLP) neural networks to model the demographic characteristics of antenatal clinic attendees in South Africa. The method of cross-validation is used to examine the betweensample variation of neural networks for HIV prediction. MLP neural networks for classifying both the HIV negative and positive clinic attendees are developed and ev...

In this paper, two feed forward neural network models have been presented to predict the Silicon Modification Level (SiML) of W319 aluminum alloys using the Thermal Analysis (T.A) parameters as inputs. The developed neural networks are a Multilayer Perceptron (MLP) network and a Radial Basis Function (RBF) network. The neural network models were found to predict the SiML accurately (R=0.99). Th...

`Only for the MLP do we include the hourly weather data as part of the input space, resulting in 25 features. And for the ResNet model, we one-hot encode day of the week and month into our input space, resulting in 41 initial input features. MULTILAYER PERCEPTRON (MLP) Our baseline of comparison is a basic MLP that consists of three fully connected layers, containing a hidden layer with 24 neur...

Multilayer perceptron network (MLP), FIR neural network and Elman neural network were compared in four different time series prediction tasks. Time series include load in an electric network series, fluctuations in a far-infrared laser series, numerically generated series and behaviour of sunspots series. FIR neural network was trained with temporal backpropagation learning algorithm. Results s...

The purpose of this study is to develop subject categorization methods for educational resources using multilayer perceptron (MLP) and to examine the performance of the test documents as an application system. To examine the performance two methods are examined: Latent Semantic Indexing method (LSI) and a three layer feedforward network as a simple MLP. The document vectors were estimated by th...

Here the authors propose the use of Fuzzy Multilayer Perceptrons for classification of land use and land cover patterns in the Brazilian Amazon, using time series of vegetation index, taken from NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. In addition to the traditional Multilayer Perceptron (MLP), three fuzzy implementations were investigated. These methods were applied...

This paper proposes an intelligent trading system using support vector regression optimized by genetic algorithms (SVR-GA) and multilayer perceptron optimized with GA (MLP-GA). Experimental results show that both approaches outperform conventional trading systems without prediction and a recent fuzzy trading system in terms of final equity and maximum drawdown for Hong Kong Hang Seng stock index.

This work explores the neural features that are trained by decreasing a discriminative energy. It directly resolves the unfaithful representation problem and the ambiguous internal representation problem in various backpropagation training algorithms for MLP. It also indirectly overcomes the premature saturation problem. Keywords—Multilayer perceptron; deep learning; Boltzmann machine; ambiguou...

A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a satisfactory performance for the training ( R 2 = 0....

A framework for Similarity-Based Methods (SBMs) includes many neural network models as special cases. Multilayer Perceptrons (MLPs) use scalar products to compute weighted activation of neurons, combining soft hyperplanes to provide decision borders. Scalar product is replaced by a distance function between the inputs and the weights, offering a natural generalization of the standard MLP model ...