scholarly journals Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Ayman Hamdy Kassem
Keyword(s):  
Neural Network ◽  
Dynamic Systems ◽  
Dynamic Models ◽  
Network Modeling ◽  
Linear Equations ◽  
Dynamics Simulation ◽  
Neural Network Modeling ◽  
The Neural Network ◽  
Speed Up ◽  
Space Dynamics

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 and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.

scholarly journals SUPERIORITY OF ARTIFICIAL NEURAL NETWORKS OVER STATISTICAL METHODS IN PREDICTION OF THE OPTIMAL LENGTH OF ROCK BOLTS

2012 ◽  
Vol 18 (5) ◽  
pp. 655-661 ◽  
Author(s):  
Hadi Hasanzadehshooiili ◽  
Ali Lakirouhani ◽  
Jurgis Medzvieckas
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Statistical Models ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Rock Bolts ◽  
Optimal Length ◽  
The Neural Network ◽  
Rock Bolting ◽  
Geological Conditions

Rock bolting is one of the most important support systems used for rock structures. Rock bolts are widely used in underground excavations as they are suitable for a wide range of geological conditions and allow using progressive design methods; besides, they help economising in the use of materials and manpower. Thus, to provide the most effective support at minimum cost by means of rock bolting, it is essential to optimise the elements contributing to bolt design, including their length, as well as bolt density and tension during installation. This paper considers the length of bolts for optimisation of the design phase, which is one of the most important parameters impacting the entire design procedure. Presenting and comparing results of some statistical models, neural network modeling is introduced as powerful means in prediction of the optimal length of rock bolts. Subsequent to training and testing of a large number of 1-layer and 2-layer backpropagation neural networks, it was reported that the optimal model was the network with the architecture of 6-18-3-1 as it demonstrated the minimum RMSE and MAE as well as the maximum R2. In comparison to statistical models (0.7182 for the value of R2 in the multiple linear regression model, 0.68 in the polynomial model and 0.7 in the dimensionless model), the results obtained by the neural network modeling – i.e. the coefficient of determination R2 of 0.9259, the value of mean absolute error MAE of 0.068, and the root mean squared error RMSE of 0.078 – not only proved their superiority but also introduced the neural network modelling as a highly capable prediction tool in forecasting the optimal length of rock bolts. Furthermore, sensitivity analysis was used to obtain parameters that have the greatest and the least impact on the optimal bolt length: the effect of the overburden thickness, tensile strength, cohesion and Poisson's ratio on the optimal bolt length was almost the same while the friction angle had the least influence.

scholarly journals FORECASTING OF THE SURFACE ROUGHNESS IN FINISHING MILLING USING NEURAL NETWORKS

2019 ◽  
Vol 4 (10) ◽  
pp. 135-141
Author(s):  
Е. Ерыгин ◽  
E. Erygin ◽  
Т. Дуюн ◽  
T. Duyun
Keyword(s):  
Neural Network ◽  
Cutting Tool ◽  
Learning Algorithm ◽  
Network Modeling ◽  
Training Sample ◽  
Neural Network Modeling ◽  
Network Learning ◽  
The Neural Network ◽  
And Training ◽  
Positive Results

This article describes the task of predicting roughness when finishing milling using neural network modeling. As a basis for the creation and training of an artificial neural network, a progressive formu-la for determining the roughness during finishing milling is chosen. The thermoEMF of the processing and processed materials is used as one of the parameters for calculating the roughness. The use of thermoEMF allows to take into account the material of the workpiece and the cutting tool, which af-fects the accuracy of the results. A training sample is created with data for five inputs and one output. The architecture, features and network learning algorithm are described. A neural network that de-termines the roughness for finishing milling has been created and configured. The process of learning and debugging of the neural network by means of graphs is clearly displayed. The network operability is checked on the test data, which allows obtaining positive results.

The Development of a Digital Twin of a Cutting Tool for Mechanical Production

2019 ◽  
pp. 11-17
Author(s):  
Y.G. Kabaldin ◽  
D.A. Shatagin ◽  
A.M. Kuzmishina
Keyword(s):  
Neural Network ◽  
Cutting Tool ◽  
Product Information ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Virtual Model ◽  
Security Issues ◽  
The Neural Network ◽  
Composition And Structure ◽  
Processing Modes

A digital model (twin) of a cutting tool based on neural network modeling is proposed in this work. It is shown that the developed virtual model makes it possible to optimize the composition and structure of wear-resistant coating and to determine the processing modes that ensure the maximum wear resistance of the cutting tool. The optimization can be performed before the actual manufacturing of the cutting tool by varying the input data of the neural network has taken place. A digital passport of the cutting tool allows the consumer to avoid buying a counterfeit product. Information security issues are considered.

scholarly journals Predictive control with mean derivative based neural euler integrator dynamic model

2007 ◽  
Vol 18 (1) ◽  
pp. 94-105 ◽  
Author(s):  
Paulo M. Tasinaffo ◽  
Atair Rios Neto
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Dynamic Systems ◽  
Attitude Control ◽  
Discrete Models ◽  
Neural Network Modeling ◽  
Feed Forward Neural Network ◽  
Orbit Transfer ◽  
Derivative Function ◽  
The Neural Network

Neural networks can be trained to get internal working models in dynamic systems control schemes. This has usually been done designing the neural network in the form of a discrete model with delayed inputs of the NARMA type (Non-linear Auto Regressive Moving Average). In recent works the use of the neural network inside the structure of ordinary differential equations (ODE) numerical integrators has also been considered to get dynamic systems discrete models. In this paper, an extension of this latter approach, where a feed forward neural network modeling mean derivatives is used in the structure of an Euler integrator, is presented and applied in a Nonlinear Predictive Control (NPC) scheme. The use of the neural network to approximate the mean derivative function, instead of the dynamic system ODE instantaneous derivative function, allows any specified accuracy to be attained in the modeling of dynamic systems with the use of a simple Euler integrator. This makes the predictive control implementation a simpler task, since it is only necessary to deal with the linear structure of a first order integrator in the calculations of control actions. To illustrate the effectiveness of the proposed approach, results of tests in a problem of orbit transfer between Earth and Mars and in a problem of three-axis attitude control of a rigid body satellite are presented.

scholarly journals Modeling the Kinetic Characteristics of the Curing Process of Polymer Composites Based on a Neural Network

2021 ◽  
Vol 27 (1) ◽  
pp. 031-041
Author(s):  
O. S. Dmitriev ◽  
◽  
A. A. Barsukov ◽  
Keyword(s):  
Neural Network ◽  
Polymer Composites ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Curing Process ◽  
Kinetic Characteristics ◽  
Degree Of Cure ◽  
Fiber Reinforced Plastic ◽  
The Neural Network ◽  
Reinforced Plastic

The article considers the possibility of modeling the kinetic characteristics of the curing process of polymer composites (for example, carbon fiber reinforced plastic) based on the use of artificial neural networks. Using neural network modeling, the dependence of the change in the kinetic function of the polymer composite on the degree of cure has been obtained. The performance of the neural network is compared with experimental data using classical approximation methods.

scholarly journals ACCURACY ESTIMATION FOR OPERATING CHARACTERISTICS NEUROMODELING OF THE OVERCURRENT PROTECTION IN A THREE PHASE MAINS

2021 ◽  
pp. 68-77
Author(s):  
Sergey O. Ivanov ◽  
Aleksandr A. Lariukhin ◽  
Maxim V. Nikandrov ◽  
Leonid A. Slavutskii
Keyword(s):  
Neural Network ◽  
Power System ◽  
Information Exchange ◽  
Network Modeling ◽  
Training Sample ◽  
Neural Network Modeling ◽  
Overcurrent Protection ◽  
The Neural Network ◽  
Three Phase ◽  
Neural Network Algorithm

Modern electric power facilities-stations and high-voltage substations have become digital objects with the active use of high-speed local networks directly involved in the technological process. Management, analysis and control of information exchange in the digital substation of the power system require the development of new tools and approaches. For these purposes, machine learning methods can be used, in particular, the artificial neural networks. The paper presents the results of neural network modeling of the operation of the overcurrent protection – as a variant of the information exchange analysis. An elementary perceptron is used as a neural network with the simplest structure. The optimized structure of the neural network and estimates of the accuracy of the neural network algorithm are given, depending on the size of the training sample (from 1000 to 50000 records), the number of training epochs. It is shown that the analysis of the neural network algorithm errors encountered during testing of the neural network enables to estimate the threshold (the setting value) current protection depending on the size of the training sample. It is found that the recognition of the protection trigger threshold in neural network modeling is violated only when the all three phase currents in electrical mains are close to the threshold. The possibilities of improving the proposed approach and its use for detecting anomalies in the information exchange and operation of secondary equipment of digital substations of the power system are discussed.

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