scholarly journals From Creativity to Artificial Neural Networks: Problem-Solving Methodologies in Hospitals

10.5772/52582 ◽  
2013 ◽  
Author(s):  
Pietro Manzi ◽  
Paolo Barbini
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Artificial Neural

scholarly journals AN ARTIFICIAL NEURAL NETWORKS MODEL FOR THE ESTIMATION OF FORMWORK LABOUR / DIRBTINIŲ NEURONINIŲ TINKLŲ MODELIS, KURIO PASKIRTIS – SKAIČIUOTI KLOJINIAMS SKIRTO DARBO APIMTIS

2011 ◽  
Vol 17 (3) ◽  
pp. 340-347 ◽  
Author(s):  
S. Umit Dikmen ◽  
Murat Sonmez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Human Brain ◽  
Field Measurements ◽  
Reinforced Concrete Frame ◽  
Concrete Frame ◽  
Frame Buildings ◽  
Actual Field ◽  
Artificial Neural

Artificial Neural Networks (ANN) is a problem solving technique imitating the basic working principles of the human brain. The formwork labour cost constitutes an important part within the costs of the reinforced concrete frame buildings. This study suggests a method based on artificial neural networks developed for estimating the required manhours for the formwork activity of such buildings. The introduced method has been verified in the study with reference to the test conducted involving two case studies. In all cases, the model produced results reasonably close to actual field measurements. The model is a simple and quick tool for the estimators and planners to aid them in their work. Santrauka Dirbtiniai neuroniniai tinklai (DNT) – tai problemų sprendimo metodas, imituojantis pagrindinius žmogaus smegenų veiklos principus. Statant gelžbetoninius karkasinius pastatus, nemažą sąnaudų dalį sudaro klojinių ruošimas. Šiame tyrime siūlomas dirbtiniais neuroniniais tinklais pagrįstas metodas, kurio paskirtis – apskaičiuoti, kiek žmogaus darbo valandų reikės ruošti klojinius tokiuose pastatuose. Pristatomas metodas tyrimo metu patikrintas remiantis bandymu, susijusiu su dviem atvejo tyrimais. Visais atvejais modelio pateikti rezultatai buvo gana artimi faktiniams matavimams. Modelis – tai paprastas ir greitai naudojamas įrankis, kuris pravers sąmatininkams ir planuotojams.

Supervised Learning with Artificial Neural Networks

2008 ◽  
pp. 24-40
Author(s):  
Darryl Charles ◽  
Colin Fyfe ◽  
Daniel Livingstone ◽  
Stephen McGlinchey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Theory And Practice ◽  
Delta Rule ◽  
Neural Learning ◽  
Artificial Neural

In this chapter we will look at supervised learning in more detail, beginning with one of the simplest (and earliest) supervised neural learning algorithms – the Delta Rule. The objectives of this chapter are to provide a solid grounding in the theory and practice of problem solving with artificial neural networks – and an appreciation of some of the challenges and practicalities involved in their use.

scholarly journals MODELLING MICROWAVE DEVICES USING ARTIFICIAL NEURAL NETWORKS / MIKROBANGŲ ĮTAISŲ MODELIAVIMAS, TAIKANT DIRBTINIŲ NEURONŲ TINKLUS

2012 ◽  
Vol 4 (1) ◽  
pp. 81-84
Author(s):  
Andrius Katkevičius
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Microwave Devices ◽  
Analysis And Synthesis ◽  
Artificial Neural

Artificial neural networks (ANN) have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics. Santrauka Nagrinėjamos dirbtinių neuronų tinklų taikymo galimybės mikrobangų įtaisams tirti. Apžvelgiami eksperimentiniai ir teoriniai darbai. Pateikiama apibendrinta informacija apie uždavinius, sprendžiamus taikant neuronų tinklus, problemas, kylančias dirbtinius neuronų tinklus taikant mikrobangų uždaviniams spręsti, ir problemų sprendimo būdus. Pateikiama bendra neuronų tinklų struktūra ir konkretūs neuronų tinklų naudojimo pavyzdžiai.

scholarly journals A Strategy for Problem Solving of Filling Imbalance in Geometrically Balanced Injection Molds

Polymers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 805
Author(s):  
Krzysztof Wilczyński ◽  
Przemysław Narowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Process Parameters ◽  
Experimental Studies ◽  
Injection Pressure ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Melt Temperature ◽  
Artificial Neural

Simulation and experimental studies were performed on filling imbalance in geometrically balanced injection molds. An original strategy for problem solving was developed to optimize the imbalance phenomenon. The phenomenon was studied both by simulation and experimentation using several different runner systems at various thermo-rheological material parameters and process operating conditions. Three optimization procedures were applied, Response Surface Methodology (RSM), Taguchi method, and Artificial Neural Networks (ANN). Operating process parameters: the injection rate, melt temperature, and mold temperature, as well as the geometry of the runner system were optimized. The imbalance of mold filling as well as the process parameters: the injection pressure, injection time, and molding temperature were optimization criteria. It was concluded that all the optimization procedures improved filling imbalance. However, the Artificial Neural Networks approach seems to be the most efficient optimization procedure, and the Brain Construction Algorithm (BSM) is proposed for problem solving of the imbalance phenomenon.

Sign in / Sign up

Export Citation Format

Share Document