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3 edition of A neural network-based estimator for the mixture ratio of the space shuttle main engine found in the catalog.

A neural network-based estimator for the mixture ratio of the space shuttle main engine

A neural network-based estimator for the mixture ratio of the space shuttle main engine

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  • 10 Currently reading

Published by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC], [Springfield, Va .
Written in English

    Subjects:
  • Neural networks (Computer science),
  • Space shuttles.

  • Edition Notes

    Other titlesNeural network based estimator for the ....
    StatementT.H. Guo and J. Musgrave.
    SeriesNASA technical memorandum -- 106070.
    ContributionsMusgrave, J., United States. National Aeronautics and Space Administration.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL18063007M

    Artificial neural network-based equation Table 1 Equations to estimate VO 2max or peak from the 20 m shuttle run test Study Sample Age (year) Input variables Equation to estimate VO 2max or peak (ml/(kg min)) Le´ger et al. [16] boys and girls 8—19 Speed and age Boys and girls: VO 2max = + S A + Many have shown the effectiveness of using neural networks for modeling time series data, and described the transformations required and limitations of such an approach. R's forecast package even implements one approach to this in the nnetar function.. Based on my reading, all of these approaches are for modeling a single outcome variable based on its past observations, but I'm .

    Calibration of Aging Diesel Engine with Artificial Neural Networks [2] Rakopoulos, C. D and Kyristsis, C. (), Development and Validation of a Comprehensive Two-Zone Model for Combustion and Emissions Formation in a Diesel Engine, International Journal of Energy Research, Vol. 27, pp [3] Vossoughi, G. R. and Rezazdeh, S. ( A Simple Neural Network Approach to Software Cost Estimation. Anupama Kaushik. α, A.K. Soni. σ & Rachna Soni. ρ. Abstract - The effort invested in a software project is one of the most challenging task and most analyzed variables in recent years in the process of project management. Software costFile Size: 1MB.

    In order to improve the precision of gas emission forecasting,this paper proposes a new forecasting model based on Particle Swarm Optimization (PSO).PSO is a novel random optimization method which has extensive capability of global the model, PSO is used to optimize the weight,width and center of RBF neural network and the optimal model is Author: Yu Min Pan, Cheng Yu Huang, Quan Zhu Zhang. detection in section Experimental models for object detection in video sequences and their evaluation methods are described in Chapter 3 and the results are shown in Chapter 4. Chapter 5 provides some thoughts on the results. Finally, Chapter 6 gives a recap of the main themes discussed in this thesis.


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A neural network-based estimator for the mixture ratio of the space shuttle main engine Download PDF EPUB FB2

Get this from a library. A neural network-based estimator for the mixture ratio of the space shuttle main engine. [T H Guo; J Musgrave; United States.

National Aeronautics and. A neural network-based estimator for the mixture ratio of the space shuttle main engine [microform] / T. Identification of space shuttle main engine dynamics [microform] / Ahmet Duyar, Ten-Huei Guo and Walter.

In literature one can find many various approaches to the tire/road friction modelling and estimation. The short overviews are given in 2 Overview of the tire/road friction modelling approaches, 3 Overview of the tire/road friction estimation approaches, network based friction force estimator, which we propose as a valuable alternative to other Cited by: marizes the main mathematical results initiated by the latter line of research, adapted mainly from [3], and serves as a convenient starting point for many possible extensions.

2 A neural network as an optimal filter Consider a dynamic environment characterized at time t by a state Xt, belonging to a set of N states, namely Xt ∈ {s1,s2 Cited by: 9. Statistical and Artificial Neural Network based Analysis of Faults in an Automobile Engine S.

Dandarea, Dr. Abstract- The paper deals with the problem of fault detection in an automobile engine using acoustic signal. The objective is to categorize the acoustic signals of engines into healthy and faulty state. Select the type of neural net for the type of regression problem to be solved, i.e.

identification of a VO 2max estimator. One of the best options for that purpose is to use a multilayered perceptron. Data preprocessing. The data gathered for this study consists of a set of instances, each instance being composed of six by:   Neural network estimator block diagram. One hidden layer with 12 processing elements and three output elements was found to give near optimal performance.

Three lines in A-vector space used in the. A neural network-based build time estimator for layer manufactured objects Article (PDF Available) in International Journal of Advanced Manufacturing Technology 57(1) November with. Estimation and there is still scope of exploring more statistical modeling approaches.

So, in this proposed study, it is tried to use Neural Network Based Approach to build a more accurate model that can improve accuracy estimates of effort required to build a software system. In this paper, however, the main focus is on investigating.

Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. R - Interpreting neural networks plot.

Ask Question Asked 4 years, 10 months ago. ping me in the comment. I run a neural network based on neuralnet to forecast. An accurate prediction of these values leads to an efficient control of the engine air fuel ratio and torque. Fuel consumption and pollutant emissions are then minimized. In this paper, we propose to use an artificial neural network- based model as a prediction tool for the engine volumetric by: 5.

Munguía J, Ciurana J, Riba C () Neural-network-based model for build-time estimation in selective laser sintering. Proc Inst Mech Eng, B: J Eng Manuf (8)– CrossRef Google Scholar Cited by: More the redundancy, the lesser the number of nodes you choose for the hidden layer so that the neural network is forced to extract the relevant features.

Conversely, if you add more nodes and layers, you allow the neural network to recombine features in new nonlinear ways. That is, you allow the network to take a new perspective. Estimation of moving agents density in 2D space based on LSTM neural network Marsela Polic, Ziad Salemy, Karlo Griparic, an artificial neural network based on LSTM architecture was designed and trained for bee density estimation.

During main advantages of a decentralized system as opposed to the. Figure 1. Efficient inference engine that works on the compressed deep neural network model for machine learning applications.

word, or speech sample. For embedded mobile applications, these resource demands become prohibitive. Table I shows the energy cost of basic arithmetic and memory operations in a 45nm CMOS process [9].

It shows that the File Size: 4MB. ARTIFICIAL NEURAL NETWORK BASED SPEED ESTIMATOR FOR VECTOR CONTROLLED INDUCTION MOTOR A. Bedri ÖZER Erhan AKIN [email protected] [email protected] Firat University, Department of Computer Engineering, ELAZIG/TURKEY Index Terms: Artificial neural networks, backpropagation algorithm, induction motor, speed sensorless, vector control.

events for training deep neural network based controllers. For example, Alpha Go [9] used deep convolutional neural networks to predict the probability of winning the game from a given state using the outcomes of several games, lots of which were playing but previous iterations of File Size: 8MB.

Given the resurgence of neural network-based techniques in recent years, it is important for data science practitioner to understand how to apply these techniques and the tradeoffs between neural network-based and traditional statistical methods.

This lecture discusses two specific techniques: Vector Autoregressive (VAR) Models and Recurrent Neural Network (RNN). Artificial neural networks Summary Objective: To develop an artificial neural network (ANN)-equation to estimate max-imal oxygen uptake (VO 2max) from 20 m shuttle run test (20mSRT) performance (stage), sex, age, weight, and height in young persons.

Methods: The 20mSRTwas performed by ( boys and 71girls) adolescents aged 13—19 years. In this research, a two-phase algorithm based on the artificial neural network (ANN) and a harmony search (HS) algorithm has been developed with the aim of assessing the reliability of structures with implicit limit state functions.

The proposed method involves the generation of datasets to be used specifically for training by Finite Element analysis, to establish an ANN.

The derivation does not require the separation of the input space by particular hyperplanes, as in previous derivations. The weights for the hidden layer can be chosen almost arbitrarily, and the weights for the output layer can be found by solving #1+1 linear equations.

The method presented exactly solves (M), the multilayer neural network Cited by: This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW-PSO).

Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW-PSO algorithm to Cited by: 4.This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation.

The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons.

For linear flight/space dynamics systems, the technique can find the network weights Cited by: 2.