Multilayered perceptron neural networks to compute energy losses in magnetic cores

2006 ◽  
Vol 307 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Ilker Kucuk
Keyword(s):  
Neural Networks ◽  
Energy Losses ◽  
Magnetic Cores ◽  
Multilayered Perceptron

scholarly journals Hybrid Neural-global Minimization Method of Logical Rule Extraction

1999 ◽  
Vol 3 (5) ◽  
pp. 348-356 ◽  
Author(s):  
Wlodzislaw Duch ◽  
◽  
Rafal Adamczak ◽  
KrzysAof Grabczewski ◽  
Grzegorz Zal
Keyword(s):  
Neural Networks ◽  
Hybrid Approach ◽  
Real Life ◽  
Rule Extraction ◽  
Global Minimization ◽  
Minimization Method ◽  
Life Problems ◽  
Multilayered Perceptron ◽  
Logical Functions ◽  
Logical Rules

Methodology of extraction of optimal sets of logical rules using neural networks and global minimization procedures has been developed. Initial rules are extracted using density estimation neural networks with rectangular functions or multilayered perceptron (MLP) networks trained with constrained backpropagation algorithm, transforming MLPs into simpler networks performing logical functions. A constructive algorithm called CMLP2LN is proposed, in which rules of increasing specificity are generated consecutively by adding more nodes to the network. Neural rule extraction is followed by optimization of rules using global minimization techniques. Estimation of confidence of various sets of rules is discussed. The hybrid approach to rule extraction has been applied to a number of benchmark and real life problems with very good results.

scholarly journals Energetic optimization and evaluation of a drinking water pumping system: application at the Rassauta station

2018 ◽  
Vol 19 (2) ◽  
pp. 472-481 ◽  
Author(s):  
Bouach Ahcene ◽  
Benmamar Saadia
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Water Demand ◽  
Forecasting Model ◽  
Energy Losses ◽  
Pumping System ◽  
Water Pumping ◽  
Pumping Stations ◽  
Regulation Model ◽  
The City

Abstract The energy overconsumption at drinking-water pumping stations creates considerable energy losses. For this reason we have developed an NNGA tool of pumping management which optimizes the consumed energy by the pumping system with respect to the hydraulic functioning conditions in the distribution tank. This tool includes two models: a forecasting model for drinking water demand based on artificial neural networks and an optimization model using genetic algorithms. The results of the NNGA tool were compared with two pumping plans: the plan based on the pumping regulation model, and the plan used by the company of water and sewage of the city of Algiers. The analysis result was done with the help of performed indicators that we have developed and which enable the evaluation and diagnosis of the energetic function's system.

scholarly journals Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Materials ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2070 ◽  
Author(s):  
Ireneusz Zagórski ◽  
Monika Kulisz ◽  
Mariusz Kłonica ◽  
Jakub Matuszak
Keyword(s):  
Neural Networks ◽  
Cutting Force ◽  
Magnesium Alloys ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Milling Process ◽  
Machining Parameters ◽  
Cutting Force Components ◽  
Multilayered Perceptron ◽  
Force Components

This paper set out to investigate the effect of cutting speed vc and trochoidal step str modification on selected machinability parameters (the cutting force components and vibration). In addition, for a more detailed analysis, selected surface roughness parameters were investigated. The research was carried out for two grades of magnesium alloys—AZ91D and AZ31—and aimed to determine stable machining parameters and to investigate the dynamics of the milling process, i.e., the resulting change in the cutting force components and in vibration. The tests were performed for the specified range of cutting parameters: vc = 400–1200 m/min and str = 5–30%. The results demonstrate a significant effect of cutting data modification on the parameter under scrutiny—the increase in vc resulted in the reduction of the cutting force components and the displacement and level of vibration recorded in tests. Selected cutting parameters were modelled by means of Statistica Artificial Neural Networks (Radial Basis Function and Multilayered Perceptron), which, furthermore, confirmed the suitability of neural networks as a tool for prediction of the cutting force and vibration in milling of magnesium alloys.

scholarly journals LTR-MDTS structure - a structure for multiple dependent time series prediction

2017 ◽  
Vol 14 (2) ◽  
pp. 467-490 ◽  
Author(s):  
Predrag Pecev ◽  
Milos Rackovic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Network Structure ◽  
Time Series Prediction ◽  
Back Propagation ◽  
Time Lapse ◽  
Multilayered Perceptron ◽  
Neural Network Structure ◽  
Mlp Neural Networks

The subject of research presented in this paper is to model a neural network structure and appropriate training algorithm that is most suited for multiple dependent time series prediction / deduction. The basic idea is to take advantage of neural networks in solving the problem of prediction of synchronized basketball referees? movement during a basketball action. Presentation of time series stemming from the aforementioned problem, by using traditional Multilayered Perceptron neural networks (MLP), leads to a sort of paradox of backward time lapse effect that certain input and hidden layers nodes have on output nodes that correspond to previous moments in time. This paper describes conducted research and analysis of different methods of overcoming the presented problem. Presented paper is essentially split into two parts. First part gives insight on efforts that are put into training set configuration on standard Multi Layered Perceptron back propagation neural networks, in order to decrease backwards time lapse effects that certain input and hidden layers nodes have on output nodes. Second part of paper focuses on the results that a new neural network structure called LTR - MDTS provides. Foundation of LTR - MDTS design relies on a foundation on standard MLP neural networks with certain, left-to-right synapse removal to eliminate aforementioned backwards time lapse effect on the output nodes.

New Aspects in the Design of Electron Optical Magnification Systems

1972 ◽  
Vol 30 ◽  
pp. 606-607
Author(s):  
Willem H.J. Andersen
Keyword(s):  
Electron Microscope ◽  
Imaging System ◽  
Chromatic Aberration ◽  
Research Tool ◽  
Energy Losses ◽  
Objective Lens ◽  
Theoretical Limit ◽  
Optical Magnification ◽  
Chromatic Aberrations ◽  
High Degree

Electron microscope design, and particularly the design of the imaging system, has reached a high degree of perfection. Present objective lenses perform up to their theoretical limit, while the whole imaging system, consisting of three or four lenses, provides very wide ranges of magnification and diffraction camera length with virtually no distortion of the image. Evolution of the electron microscope in to a routine research tool in which objects of steadily increasing thickness are investigated, has made it necessary for the designer to pay special attention to the chromatic aberrations of the magnification system (as distinct from the chromatic aberration of the objective lens). These chromatic aberrations cause edge un-sharpness of the image due to electrons which have suffered energy losses in the object.There exist two kinds of chromatic aberration of the magnification system; the chromatic change of magnification, characterized by the coefficient Cm, and the chromatic change of rotation given by Cp.

Localization of DNA in chromatin using ESI and osmium ammine staining

1990 ◽  
Vol 48 (3) ◽  
pp. 116-117
Author(s):  
C.L. Woodcock ◽  
R.A. Horowitz ◽  
D. P. Bazett-Jones ◽  
A.L. Olins
Keyword(s):  
Spectroscopic Imaging ◽  
Energy Losses ◽  
Electron Spectroscopic Imaging ◽  
Feulgen Reaction ◽  
Specific Location ◽  
Eukaryotic Nucleus ◽  
Chromatin Fibers ◽  
Patiria Miniata ◽  
Model Structures

In the eukaryotic nucleus, DNA is packaged into nucleosomes, and the nucleosome chain folded into ‘30nm’ chromatin fibers. A number of different model structures, each with a specific location of nucleosomal and linker DNA have been proposed for the arrangment of nucleosomes within the fiber. We are exploring two strategies for testing the models by localizing DNA within chromatin: electron spectroscopic imaging (ESI) of phosphorus atoms, and osmium ammine (OSAM) staining, a method based on the DNA-specific Feulgen reaction.Sperm were obtained from Patiria miniata (starfish), fixed in 2% GA in 150mM NaCl, 15mM HEPES pH 8.0, and embedded In Lowiciyl K11M at -55C. For OSAM staining, sections 100nm to 150nm thick were treated as described, and stereo pairs recorded at 40,000x and 100KV using a Philips CM10 TEM. (The new osmium ammine-B stain is available from Polysciences Inc). Uranyl-lead (U-Pb) staining was as described. ESI was carried out on unstained, very thin (<30 nm) beveled sections at 80KV using a Zeiss EM902. Images were recorded at 20,000x and 30,000x with median energy losses of 110eV, 120eV and 160eV, and a window of 20eV.

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