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...

Materials and methods: A data set consisting of 584 stroke patients was analyzed using MLP neural networks. Th e eff ect of prognostic factors (age, hospitalization time, sex, hypertension, atrial fi brillation, embolism, stroke type, infection, diabetes mellitus, and ischemic heart disease) on mortality in stroke were trained with 6 diff erent MLP algorithms [quick propagation (QP), Levenberg-...

Link admission control (LAC) in broadband ATM networks is based on evaluation of expected traffic performance. The traditional LAC approach relies on approximate analytical performance models, and can lead to an over controlled network. This paper presents a hybrid LAC scheme which uses a multi layer perceptron (MLP) to refine the performance estimate of a traditional analytical approximation. ...

`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...

Gastrointestinal (GI) tract involvement of mantle cell lymphoma (MCL) presents as a variety of forms, ranging from multiple lymphomatous polyposis (MLP) to a slight mucosal change. We report 3 cases with GI tract involvement of MCL who were followed-up by endoscopy. The present study shows three new informations. MLP of the esophagus is rare, but it was observed in two of 3 patients who were ex...

When designing artificial neural network (ANN) it is important to optimise the network architecture and the learning coefficients of the training algorithm, as well as the time the network training phase takes, since this is the more timeconsuming phase. In this paper an approach to cooperative co-evolutionary optimisation of multilayer perceptrons (MLP) is presented. The cooperative co-evoluti...

The complexity and high dimensions of big data sonar, as well the unavoidable presence unwanted signals such noise, clutter, reverberation in environment sonar propagation, have made classification one most interesting applicable topics for active researchers this field. This paper proposes use Grasshopper Optimization Algorithm (GOA) to train Multilayer Perceptron Artificial Neural Network (ML...

Recently, many convolutional neural network (CNN)-based methods have been proposed to tackle the classification task of hyperspectral images (HSI). In fact, CNN has become de-facto standard for HSI classification. It seems that traditional networks such as multi-layer perceptron (MLP) are not competitive However, in this study, we try prove MLP can achieve good performance if it is properly des...

In this paper we propose a new multilayer classifier architecture. The proposed hybrid architecture has two cascaded modules: feature extraction module and classification module. In the feature extraction module we use the multilayered perceptron (MLP) neural networks, although other tools such as radial basis function (RBF) networks can be used. In the classification module we use support vect...