Using Artificial Neural Networks as a Forward Approach to Backcalculation

1997 ◽  
Vol 1570 (1) ◽  
pp. 126-133 ◽  
Author(s):  
Roger W. Meier ◽  
Don R. Alexander ◽  
Reed B. Freeman
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Models ◽  
Forward Problem ◽  
Problem Solver ◽  
Network Training ◽  
Test Conditions ◽  
The Neural Networks ◽  
Artificial Neural

In recent years, artificial neural networks have successfully been trained to backcalculate pavement layer moduli from the results of falling weight deflectometer (FWD) tests. These neural networks provide the same solutions as existing programs, only thousands of times faster. Unfortunately, their use is constrained to the test conditions assumed during network training. These limitations arise from practical aspects of neural network training and cannot be circumvented easily. The goal of this research was to develop a backcalculation program combining the speed of neural networks and the flexibility of conventional programs to produce the same solutions as existing programs. This was accomplished by forgoing neural network backcalculation in favor of neural network forward-calculation, that is, using neural networks in place of complex numerical models for computing the forward-problem solutions used by the conventional backcalculation programs. A suite of neural networks, covering a range of flexible pavement structures, was trained using data generated by WESLEA, the forward-problem solver used in the WESDEF backcalculation program. When tested on 110 experimental FWD results, a version of WESDEF augmented by the neural networks provided statistically identical answers 42 times faster, on average, than the original. Provisions have been made for periodic upgrades as additional networks are trained for other pavement types and test conditions. Meanwhile, the original WESLEA can still be used when an appropriate network is unavailable. This preserves the flexibility of the original program while taking maximum advantage of the speed gains afforded by the neural networks.

scholarly journals Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Aminmohammad Saberian ◽  
H. Hizam ◽  
M. A. M. Radzi ◽  
M. Z. A. Ab Kadir ◽  
Maryam Mirzaei
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
General Regression Neural Network ◽  
Maximum Temperature ◽  
Photovoltaic Panel ◽  
The Neural Networks ◽  
Artificial Neural ◽  
General Regression

This paper presents a solar power modelling method using artificial neural networks (ANNs). Two neural network structures, namely, general regression neural network (GRNN) feedforward back propagation (FFBP), have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

Establish an Artificial Neural Networks Model to Make Quantitative Analysis about the Capillary Electrophoresis Spectrum

2012 ◽  
Vol 452-453 ◽  
pp. 1116-1120
Author(s):  
Hong Ping Li ◽  
Hong Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Capillary Electrophoresis ◽  
Artificial Neural Networks ◽  
Quantitative Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Network Training ◽  
Dissociation Degree ◽  
Artificial Neural

Simulating the overlapping capillary electrophoresis spectrogram under the dissimilar conditions by the computer system , Choosing the overlapping capillary electrophoresis spectrogram simulated under the different conditions , processing the data to compose a neural network training regulations, Applying the artificial neural networks method to make a quantitative analysis about the multi-component in the overlapping capillary electrophoresis spectrogram,Using: Radial direction primary function neural network model and multi-layered perceptron neural network model. The findings indicated that, along with the increasing of the capillary electrophoresis spectrogram noise level, the related components’ ability of the two kinds of the overlapping capillary electrophoresis spectrogram by neural network model quantitative analysis drop down. Along with the increasing of the capillary electrophoresis spectrogram’s total dissociation degree, the multi-layered perceptron neural network model to the related components’ ability of the overlapping capillary electrophoresis spectum by quantitative analysis raise up.

Using Artificial Bee Colony to Improve Functional Link Neural Network Training

2012 ◽  
Vol 263-266 ◽  
pp. 2102-2108 ◽  
Author(s):  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Bee Colony ◽  
Neural Network Training ◽  
Functional Link ◽  
Bee Colony ◽  
Network Training ◽  
Artificial Neural ◽  
Functional Link Neural Network

Artificial Neural Networks have emerged as an important tool for classification and have been widely used to classify non-linearly separable pattern. The most popular artificial neural networks model is a Multilayer Perceptron (MLP) that is able to perform classification task with significant success. However due to the complexity of MLP structure and also problems such as local minima trapping, over fitting and weight interference have made neural network training difficult. Thus, the easy way to avoid these problems is by removing the hidden layers. This paper presents the ability of Functional Link Neural Network (FLNN) in overcoming the complexity structure of MLP, using it single layer architecture and proposes an Artificial Bee Colony (ABC) optimization for training the FLNN. The proposed technique is expected to provide better learning scheme for a classifier in order to get more accurate classification result.

scholarly journals ARTIFICIAL NEURAL NETWORKS IN FOREX CURRENCY MARKETS

2021 ◽  
Vol 10 (5) ◽  
pp. 89-92
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Daily Life ◽  
Currency Markets ◽  
Artificial Neurons ◽  
The Neural Networks ◽  
Artificial Neural

The Artificial Intelligence is growing and covering various aspects of our daily life. The idea seems to be very complex. It seems that a program cannot be developed using our home PC. But believe me, it's not that difficult. Let us try to understand what the neural networks are and how they can be applied in trading. Artificial Neural Networks can be used in forex Currency Trading, for finding or predicting the next possible movements. We know that Artificial Neural Networks involves study of neurons in the human brain, sometimes called as biological network.ANN is based on connections of nodes, units called as artificial nodes or neurons. Neural network is an entity consisting of artificial neurons, among which there is an organized relationship. These relations are similar to a biological brain.

scholarly journals OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks

2016 ◽  
Author(s):  
Serrano Pereira ◽  
Barbara Gravendeel ◽  
Patrick Wijntjes ◽  
Rutger A. Vos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Web Application ◽  
Image Features ◽  
Identification System ◽  
Taxonomic Level ◽  
Network Training ◽  
Artificial Neural

ABSTRACTTaxonomic expertise for the identification of species is rare and costly. On-going advances in computer vision and machine learning have led to the development of numerous semi- and fully automated species identification systems. However, these systems are rarely agnostic to specific morphology, rarely can perform taxonomic “approximation” (by which we mean partial identification at least to higher taxonomic level if not to species), and frequently rely on costly scientific imaging technologies. We present a generic, hierarchical identification system for automated taxonomic approximation of organisms from images. We assessed the effectiveness of this system using photographs of slipper orchids (Cypripedioideae), for which we implemented image pre-processing, segmentation, and colour and shape feature extraction algorithms to obtain digital phenotypes for 116 species. The identification system trained on these digital phenotypes uses a nested hierarchy of artificial neural networks for pattern recognition and automated classification that mirrors the Linnean taxonomy, such that user-submitted photos can be assigned a genus, section, and species classification by traversing this hierarchy. Performance of the identification system varied depending on photo quality, number of species included for training, and desired taxonomic level for identification. High quality photos were scarce for some taxa and were under-represented in the training set, resulting in imbalanced network training. The image features used for training were sufficient to reliably identify photos to the correct genus but less so to the correct section and species. The outcomes of this project include a library of feature extraction algorithms called ImgPheno, a collection of scripts for neural network training called NBClassify, a library for evolutionary optimization of artificial neural network construction called AI::FANN::Evolving and a planned web application called OrchID for identification of user-submitted images. All project outcomes are open source and freely available.

scholarly journals Accelerated optimizations of an electromagnetic acoustic transducer with artificial neural networks as metamodels

2017 ◽  
Vol 6 (2) ◽  
pp. 269-284
Author(s):  
Shen Wang ◽  
Songling Huang ◽  
Qing Wang ◽  
Lisha Peng ◽  
Wei Zhao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Radial Basis Function ◽  
Objective Function ◽  
Basis Function ◽  
Numerical Models ◽  
Displacement Component ◽  
The Neural Network ◽  
Artificial Neural

Abstract. Electromagnetic acoustic transducers (EMATs) are noncontact transducers generating ultrasonic waves directly in the conductive sample. Despite the advantages, their transduction efficiencies are relatively low, so it is imperative to build accurate multiphysics models of EMATs and optimize the structural parameters accordingly, using a suitable optimization algorithm. The optimizing process often involves a large number of runs of the computationally expensive numerical models, so metamodels as substitutes for the real numerical models are helpful for the optimizations. In this work the focus is on the artificial neural networks as the metamodels of an omnidirectional EMAT, including the multilayer feedforward networks trained with the basic and improved back propagation algorithms and the radial basis function networks with exact and nonexact interpolations. The developed neural-network programs are tested on an example problem. Then the model of an omnidirectional EMAT generating Lamb waves in a linearized steel plate is introduced, and various approaches to calculate the amplitudes of the displacement component waveforms are discussed. The neural-network metamodels are then built for the EMAT model and compared to the displacement component amplitude (or ratio of amplitudes) surface data on a discrete grid of the design variables as the reference, applying a multifrequency model with FFT (fast Fourier transform)/IFFT (inverse FFT) processing. Finally the two-objective optimization problem is formulated with one objective function minimizing the ratio of the amplitude of the S0-mode Lamb wave to that of the A0 mode, and the other objective function minimizing as the negative amplitude of the A0 mode. Pareto fronts in the criterion space are solved with the neural-network models and the total time consumption is greatly decreased. From the study it could be observed that the radial basis function network with exact interpolation has the best performance considering its accuracy of approximation and the time required to build the metamodel.

The Use of Artificial Neural Networks in the Design of Aerodynamic Profiles of a Rotor of a Helicopter

2020 ◽  
pp. 16-27
Author(s):  
V.A. Anikin ◽  
Ya.A. Indrulenayte ◽  
O.A. Pashkov ◽  
Yu.N. Sviridenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Calculated Data ◽  
Aerodynamic Characteristics ◽  
Design System ◽  
Distinctive Features ◽  
Network Training ◽  
Pitch Moment ◽  
Artificial Neural

The authors showed the possibility of using mathematical models based on artificial neural networks to determine the aerodynamic characteristics of helicopter profiles, as well as the ability to design new pro-files with specified aerodynamic characteristics. At the first stage of work, an approximation model based on a neural network of the multilayer perceptron type was created to determine the coefficients of lift, drag, and pitch moment of the profiles. This topology has a number of distinctive features and is well suited for solving such problems. Neural network training was conducted. As a training set, the calculated data of 3692 aerodynamic profiles were used. The accuracy of the approximation of aerodynamic characteristics was estimated. The expediency of using artificial neural networks to solve this class of problems was substantiated. At the second stage of work, to obtain the geometry of new profiles, a mathematical model was created on the basis of special classes of artificial replicative neural networks, which allowed us to significantly reduce the dimension of the space used to describe the surface of the aerodynamic profile and create a qualitatively new design system. Examples were given of using the system for creating profile families in the region of specified aerodynamic characteristics and limiting the maximum relative thickness of the profile

scholarly journals Classification of asteroid families with artificial neural networks

2020 ◽  
pp. 39-48
Author(s):  
D. Vujicic ◽  
R. Pavlovic ◽  
D. Milosevic ◽  
B. Djordjevic ◽  
S. Randjic ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Neural Network Training ◽  
Network Training ◽  
Artificial Neural ◽  
Artificial Neural Network Training ◽  
Asteroid Families

This paper describes an artificial neural network for classification of asteroids into families. The data used for artificial neural network training and testing were obtained by the Hierarchical Clustering Method (HCM). We have shown that an artificial neural networks can be used as a validation method for the HCM on families with a large number of members.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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