This paper describes construction of multilayer perceptron by open source neural networks simulator Neuroph and Petri net. The described multilayer perceptron solves logical function "xor "exclusive or. The aim is to explore the possibilities of description of the neural networks by Petri Nets. The selected neural network (multilayer perceptron) allows to be seen clearly the advantages and disa...

The estimation and forecasting of the hydrologic data carry significance for many water resources engineering problems. Establishing sediment monitoring instruments on rivers is a costly operation. The methods available in literature for sediment concentration estimation are complicated, time consuming and necessitate cumbersome parameter estimation procedures. Artificial neural networks have b...

The purpose of this paper is to provide a quick overview of neural networks and to explain how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorithm (including its variations) is the principal procedure for training multilayer perceptrons; it is briefly described here. ...

Finding suitable jobs for US Navy sailors periodically is an important and everchanging process. An Intelligent Distribution Agent (IDA) and particularly its constraint satisfaction module take up the challenge to automate the process. The constraint satisfaction module's main task is to provide the bulk of the decision making process in assigning sailors to new jobs in order to maximize Navy a...

This paper presents a novel hybrid method for short-term load forecasting. The system comprises of two artificial neural networks (ANN), assembled in a hierarchical order. The first ANN is a multilayer perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor...

An Artificial Neural Network (ANN) is designed to investigate its application for data assimilation. This procedure provides an appropriated initial condition to the atmosphere to weather forecasting. Data assimilation is a method to insert observational information into a physicalmathematical model. The goal here is the process for assimilating meteorological observations. The numerical experi...

A multilayer perceptron is usually considered a passive learner that only receives given training data. However, if a multilayer perceptron actively gathers training data that resolve its uncertainty about a problem being learnt, sufficiently accurate classification is attained with fewer training data. Recently, such active learning has been receiving an increasing interest. In this paper, we ...

This work presents a constructive method to train the multilayer perceptron layer after layer successively and to accomplish the kernel used in the support vector machine. Data in different classes will be trained to map to distant points in each layer. This will ease the mapping of the next layer. A perfect mapping kernel can be accomplished successively. Those distant mapped points can be dis...

Label Ranking problems are receiving increasing attention in machine learning. The goal is to predict not just a single value from a finite set of labels, but rather the permutation of that set that applies to a new example (e.g., the ranking of a set of financial analysts in terms of the quality of their recommendations). In this paper, we adapt a multilayer perceptron algorithm for label rank...