Creating surrogate models for an air and missile defense simulation using design of experiments and neural networks

2019 ◽  
pp. 154851291987798
Author(s):  
Brian M Wade
Keyword(s):  
Design Of Experiments ◽  
Network Models ◽  
Surrogate Models ◽  
Cruise Missile ◽  
Missile Defense ◽  
Neural Network Models ◽  
Desktop Computer ◽  
Air Defense ◽  
The Mean ◽  
Cruise Missiles

This paper demonstrates a method of constructing multiple linked surrogate models of a high-fidelity air and missile defense simulation using design of experiments to generate labeled data for neural network models. The surrogate models are used to predict the number of incoming missiles destroyed and the number of interceptors launched from a multi-layered defense composed of three different air defense systems intercepting both ballistic and cruise missiles without the need for time intensive simulation runs. A single model that predicts all outcomes was first attempted, but was shown to have inadequate prediction capabilities. The working setup uses multiple surrogate models that are linked to allow information to pass between each model. The paper demonstrates how to develop the surrogate models using a notional example, and how to link these surrogate models together using time to impact for the missiles. The same methodology also allows the same surrogate model to switch between ballistic and cruise missile engagements. When run on a desktop computer, a 30 Monte Carlo set of the notional example took several minutes to complete; however, this proof of principal implementation of the surrogate models was able to predict the mean number missiles destroyed or the mean number of interceptors fired to within one missile nearly instantaneously.

scholarly journals Real-Time Guitar Amplifier Emulation with Deep Learning

2020 ◽  
Vol 10 (3) ◽  
pp. 766 ◽  
Author(s):  
Alec Wright ◽  
Eero-Pekka Damskägg ◽  
Lauri Juvela ◽  
Vesa Välimäki
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Reference Signal ◽  
Network Models ◽  
Neural Network Models ◽  
Desktop Computer ◽  
The Neural Network ◽  
Highly Nonlinear ◽  
The Difference

This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on the WaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for the WaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.

Shape Optimization of Turbomachinery Blade Using Multiple Surrogate Models

2006 ◽  
Author(s):  
Abdus Samad ◽  
Kwang-Yong Kim ◽  
Tushar Goel ◽  
Raphael T. Haftka ◽  
Wei Shyy
Keyword(s):  
Shape Optimization ◽  
Cost Benefit Analysis ◽  
Weighted Average ◽  
Cost Benefit ◽  
Network Models ◽  
Surrogate Models ◽  
Fractional Factorial ◽  
Neural Network Models ◽  
Optimal Point ◽  
Multiple Surrogate Models

Performances of multiple surrogate models are evaluated in a turbomachinery blade shape optimization. The basic models, i.e., Response Surface Approximation, Kriging and Radial Basis Neural Network models as well as weighted average models are tested for shape optimization. Global data based errors for each surrogates are used to calculate the weights. These weights are multiplied with the respective surrogates to get the final weighted average models. Sequential Quadratic Programming is used to search the optimal point from these constructed surrogates. Use of multiple surrogates via weighted averaged surrogates gives more robust approximation than individual surrogates. Three design variables are selected to enhance the performance of transonic axial compressor (NASA rotor 37) blade and the design points are selected using three level fractional factorial D-optimal designs. The performance of compressor is improved by optimization because of reduction of losses and movement of separation line towards down stream directions. The present approach can help address the multi-objective design on a rational basis with quantifiable cost-benefit analysis.

Neural network models for prediction of trichothecene content in wheat

2008 ◽  
Vol 1 (3) ◽  
pp. 349-356 ◽  
Author(s):  
F. Mateo ◽  
R. Gadea ◽  
R. Mateo ◽  
A. Medina ◽  
F. Valle-Algarra ◽  
...  
Keyword(s):  
Animal Health ◽  
Fungal Growth ◽  
Network Models ◽  
Data Matrix ◽  
Electron Capture Detection ◽  
Contamination Level ◽  
Fungal Contamination ◽  
Neural Network Models ◽  
The Mean ◽  
First Time

Fusarium graminearum is a mould that causes serious diseases in cereals worldwide and that synthesises mycotoxins such as deoxynivalenol (DON), which can seriously affect human and animal health. Predicting the level of mycotoxin accumulation in food is very difficult, because of the complexity of the influencing parameters. In this work, we have studied the possibility of using artificial neural networks (NN) to predict DON level attained in F. graminearum wheat cultures taking as inputs the fungal contamination level of the cereal, the water activity as a measure of the available water for fungal growth in the cereal, the temperature and time. DON analysis was performed by gas chromatography with electron capture detection. The data matrix was used to train and validate various types of NN using MATLAB 7.0. The aim was to obtain a network that provided the best possible fit between predicted and target DON levels by minimising the mean-square error of test. Radial basis function-NNs attained lower errors and better generalisation than multi-layer perceptron networks to predict DON accumulation in wheat. This is the first time that NNs have been used to predict DON accumulation in wheat based on the studied factors.

Prediction of pressure fluctuations on sloping stilling basins

2006 ◽  
Vol 33 (11) ◽  
pp. 1379-1388 ◽  
Author(s):  
A Güven ◽  
M Günal ◽  
A Çevik
Keyword(s):  
Neural Network ◽  
Pressure Fluctuation ◽  
Hydraulic Jump ◽  
Regression Models ◽  
Pressure Fluctuations ◽  
Network Models ◽  
Neural Network Models ◽  
Mean Pressure ◽  
Stilling Basins ◽  
The Mean

Various types of hydraulic jump occurring on horizontal and sloping channels have been analyzed experimentally, theoretically, and numerically and the results are available in the literature. In this study, artificial neural network models were developed to simulate the mean pressure fluctuations beneath a hydraulic jump occurring on sloping stilling basins. Multilayers feed a forward neural network with a back-propagation learning algorithm to model the pressure fluctuations beneath such a type of hydraulic jump (B-jump). An explicit formula that predicts the mean pressure fluctuation in terms of the characteristics that contribute most to the hydraulic jump occurring on the sloping basins is presented. The proposed neural network models are compared with linear and nonlinear regression models that were developed using considered physical parameters. The results of the neural network modelling are found to be superior to the regression models and are in good agreement with the experimental results due to relatively small values of error (mean absolute percentage error).Key words: neural networks, pressure fluctuation, hydraulic jump, sloping stilling basin, explicit NN formulation, regression analysis.

A multi-objective optimization of ballistic and cruise missile fire plans based on damage calculations from missile impacts on an airfield defended by an air defense artillery network

2018 ◽  
Vol 16 (2) ◽  
pp. 103-117
Author(s):  
Brian M Wade
Keyword(s):  
Cruise Missile ◽  
Multi Objective Optimization ◽  
Air Defense ◽  
Multi Objective ◽  
Damage Models ◽  
Cruise Missiles ◽  
Different Parts

This paper presents a new methodology for optimizing missile fire plan parameters in order to maximize the damage caused to an airfield by the impacts of ballistic and cruise missiles that leak through an air defense artillery network using weaponeering calculations. This new methodology models the damage to the airfield runway, fuel infrastructure, and parked aircraft caused by both unitary and submunition warheads carried by the ballistic and cruise missiles. It then uses the airfield damage models paired with an air defense simulation in a multi-objective optimization to find the non-dominated frontier of missile fire plan parameters that inflict the greatest damage to the different parts of the defended airfield. It demonstrates the methodology for three increasingly sized ballistic and cruise missile fire plans.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

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