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1 – 10 of over 2000
Article
Publication date: 6 March 2017

Ney Rafael Secco and Bento Silva de Mattos

Multidisciplinary design frameworks elaborated for aeronautical applications require considerable computational power that grows enormously with the utilization of higher fidelity…

Abstract

Purpose

Multidisciplinary design frameworks elaborated for aeronautical applications require considerable computational power that grows enormously with the utilization of higher fidelity tools to model aeronautical disciplines like aerodynamics, loads, flight dynamics, performance, structural analysis and others. Surrogate models are a good alternative to address properly and elegantly this issue. With regard to this issue, the purpose of this paper is the design and application of an artificial neural network to predict aerodynamic coefficients of transport airplanes. The neural network must be fed with calculations from computational fluid dynamic codes. The artificial neural network system that was then developed can predict lift and drag coefficients for wing-fuselage configurations with high accuracy. The input parameters for the neural network are the wing planform, airfoil geometry and flight condition. An aerodynamic database consisting of approximately 100,000 cases calculated with a full-potential code with computation of viscous effects was used for the neural network training, which is carried out with the back-propagation algorithm, the scaled gradient algorithm and the Nguyen–Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error. The neural network featuring the lowest regression error is able to reduce the computation time of the aerodynamic coefficients 4,000 times when compared with the computing time required by the full potential code. Regarding the drag coefficient, the average error of the neural network is of five drag counts only. The computation of the gradients of the neural network outputs in a scalable manner is possible by an adaptation of back-propagation algorithm. This enabled its use in an adjoint method, elaborated by the authors and used for an airplane optimization task. The results from that optimization were compared with similar tasks performed by calling the full potential code in another optimization application. The resulting geometry obtained with the aerodynamic coefficient predicted by the neural network is practically the same of that designed directly by the call of the full potential code.

Design/methodology/approach

The aerodynamic database required for the neural network training was generated with a full-potential multiblock-structured code. The training process used the back-propagation algorithm, the scaled-conjugate gradient algorithm and the Nguyen–Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error.

Findings

A suitable and efficient methodology to model aerodynamic coefficients based on artificial neural networks was obtained. This work also suggests appropriate sizes of artificial neural networks for this specific application. We demonstrated that these metamodels for airplane optimization tasks can be used without loss of fidelity and with great accuracy, as their local minima might be relatively close to the minima of the original design space defined by the call of computational fluid dynamics codes.

Research limitations/implications

The present work demonstrated the ability of a metamodel with artificial neural networks to capture the physics of transonic and subsonic flow over a wing-fuselage combination. The formulation that was used was the full potential equation. However, the present methodology can be extended to model more complex formulations such as the Euler and Navier–Stokes ones.

Practical implications

Optimum networks reduced the computation time for aerodynamic coefficient calculations by 4,000 times when compared with the full-potential code. The average absolute errors obtained were of 0.004 and 0.0005 for lift and drag coefficient prediction, respectively. Airplane configurations can be evaluated more quickly.

Social implications

If multidisciplinary optimization tasks for airplane design become more efficient, this means that more efficient airplanes (for instance less polluting airplanes) can be designed. This leads to a more sustainable aviation.

Originality/value

This research started in 2005 with a master thesis. It was steadily improved with more efficient artificial neural networks able to handle more complex airplane geometries. There is a single work using similar techniques found in a conference paper published in 2007. However, that paper focused on the application, i.e. providing very few details of the methodology to model aerodynamic coefficients.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

Book part
Publication date: 14 November 2022

Krishna Teja Perannagari and Shaphali Gupta

Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical…

Abstract

Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical problems. ANN applications have been employed in various disciplines such as psychology, computer science, mathematics, engineering, medicine, manufacturing, and business studies. Academic research on ANNs is witnessing considerable publication activity, and there exists a need to track the intellectual structure of the existing research for a better comprehension of the domain. The current study uses a bibliometric approach to ANN business literature extracted from the Web of Science database. The study also performs a chronological review using science mapping and examines the evolution trajectory to determine research areas relevant to future research. The authors suggest that researchers focus on ANN deep learning models as the bibliometric results predict an expeditious growth of the research topic in the upcoming years. The findings reveal that business research on ANNs is flourishing and suggest further work on domains, such as back-propagation neural networks, support vector machines, and predictive modeling. By providing a systematic and dynamic understanding of ANN business research, the current study enhances the readers' understanding of existing reviews and complements the domain knowledge.

Details

Exploring the Latest Trends in Management Literature
Type: Book
ISBN: 978-1-80262-357-4

Keywords

Article
Publication date: 1 July 2000

M.L. Nasir, R.I. John, S.C. Bennett, D.M. Russell and A Patel

An appropriate use of neural computing techniques is to apply them to corporate bankruptcy prediction, where conventional solutions can be hard to obtain. Having said that…

1007

Abstract

An appropriate use of neural computing techniques is to apply them to corporate bankruptcy prediction, where conventional solutions can be hard to obtain. Having said that, choosing an appropriate Artificial Neural Network topology (ANN) for predicting corporate bankruptcy would remain a daunting prospect. The context of the problem is that there are no fixed rules in determining the ANN structure or its parameter values, a large number of ANN topologies may have to be constructed with different structures and parameters before determining an acceptable model. The trial‐and‐error process can be tedious, and the experience of the ANN user in constructing the topologies is invaluable in the search for a good model. Yet, a permanent solution does not exist. This paper identifies a non trivial novel approach for implementing artificial neural networks for the prediction of corporate bankruptcy by applying inter‐connected neural networks. The proposed approach is to produce a neural network architecture that captures the underlying characteristics of the problem domain. The research primarily employed financial data sets from the London Stock Exchange and Jordans financial database of major public and private British companies. Early results indicate that an ANN appears to outperform the traditional approach in forecasting corporate bankruptcy.

Details

Journal of Applied Accounting Research, vol. 5 no. 3
Type: Research Article
ISSN: 0967-5426

Keywords

Article
Publication date: 1 March 2000

K.C. LAM, S. THOMAS NG, TIESONG HU, MARTIN SKITMORE and S.O. CHEUNG

The selection criteria for contractor pre‐qualification are characterized by the co‐existence of both quantitative and qualitative data. The qualitative data is non‐linear…

Abstract

The selection criteria for contractor pre‐qualification are characterized by the co‐existence of both quantitative and qualitative data. The qualitative data is non‐linear, uncertain and imprecise. An ideal decision support system for contractor pre‐qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated non‐linear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre‐qualification criteria (variables) were identified for the model. One hundred and twelve real pre‐qualification cases were collected from civil engineering projects in Hong Kong, and 88 hypothetical pre‐qualification cases were also generated according to the ‘If‐then’ rules used by professionals in the pre‐qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre‐qualification case consisted of input ratings for candidate contractors' attributes and their corresponding pre‐qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross‐validation was applied to estimate the generalization errors based on the ‘re‐sampling’ of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated non‐linear relationship between contractors' attributes and their corresponding pre‐qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre‐qualification task.

Details

Engineering, Construction and Architectural Management, vol. 7 no. 3
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 3 January 2017

Jiaqi Jia and Haibin Duan

The purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural network

Abstract

Purpose

The purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural network (BP-ANN) algorithm, with the objective of optimizing the structure of backpropagation network, to increase the efficiency and decrease the recognition time. A hardware-in-the-loop system for UAV target automatic recognition is also developed.

Design/methodology/approach

The hybrid model of BP-ANN structure is established for aircraft automatic target recognition. This proposed method identifies controller parameters and reduces the computational complexity. Approaching speed of the network is faster and recognition accuracy is higher. This kind of network combines or better fuses the advantages of backpropagation artificial neural algorithm and Hu moment. with advantages of two networks and improves the speed and accuracy of identification. Finally, a hardware-in-the-loop system for UAV target automatic recognition is also developed.

Findings

The double hidden level backpropagation artificial neural can easily increase the speed of recognition process and get a good performance for recognition accuracy.

Research limitations/implications

The proposed backpropagation artificial neural algorithm can be ANN easily applied to practice and can help the design of the aircraft automatic target recognition system. The standard backpropagation algorithm has some obvious drawbacks, namely, converging slowly and falling into the local minimum point easily. In this paper, an improved algorithm based on the standard backpropagation algorithm is constructed to make the aircraft target recognition more practicable.

Originality/value

A double hidden levels backpropagation artificial neural algorithm is presented for automatic target recognition system of UAV.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 1
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 1 February 1999

SDHABHON BHOKHA and STEPHEN O. OGUNLANA

The application of an artificial neural network (ANN) to forecast the construction duration of buildings at the predesign stage is described in this paper. A three‐layered…

Abstract

The application of an artificial neural network (ANN) to forecast the construction duration of buildings at the predesign stage is described in this paper. A three‐layered back‐propagation (BP) network consisting of 11 input nodes has been constructed. Ten binary input nodes represent basic information on building features (i.e. building function, structural system, foundation, height, exterior finishing, quality of interior decorating, and accessibility to the site), and one real‐value input represents functional area. The input nodes are fully connected to one output node through hidden nodes. The network was implemented on a Pentium‐150 based microcomputer using a neurocomputer program written in C+ +. The Generalized Delta Rule (GDR) was used as learning algorithm. One hundred and thirty‐six buildings built during the period 1987–95 in the Greater Bangkok area were used for training and testing the network. The determination of the optimum number of hidden nodes, learning rate, and momentum were based on trial‐and‐error. The best network was found to consist of six hidden nodes, with a learning rate of 0.6, and null momentum. It was trained for 44700 epochs within 943 s such that the mean squared error (judgement) of training and test samples were reduced to 1.17 × 10−7 and 3.10 × 10−6, respectively. The network can forecast construction du‐ration at the predesign stage with an average error of 13.6%.

Details

Engineering, Construction and Architectural Management, vol. 6 no. 2
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 12 April 2022

Monica Puri Sikka, Alok Sarkar and Samridhi Garg

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been…

1184

Abstract

Purpose

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

Design/methodology/approach

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

Findings

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

Originality/value

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

Details

Research Journal of Textile and Apparel, vol. 28 no. 1
Type: Research Article
ISSN: 1560-6074

Keywords

Article
Publication date: 1 December 2000

Amitava Chatterjee, O.Felix Ayadi and Bryan E. Boone

This study describes the structure and function of a new financial modeling technique, namely, the Artificial Neutral Network (ANN) in predicting financial markets’ behavior. With…

1226

Abstract

This study describes the structure and function of a new financial modeling technique, namely, the Artificial Neutral Network (ANN) in predicting financial markets’ behavior. With the advancement of the computer technology to date, ANN allows us to imitate human reasoning and thought processes in identifying the optimal trading strategies in the financial markets. The paper identifies the theory and steps involved in performing ANN and Generic Alogorithm in financial markets, the accuracy of the computer learning process, and the appropriate ways to use this process in developing trading strategies. It further discusses the superiority of ANN over traditional methodologies. The study concludes with the description of successful use of ANN by various financial institutions.

Details

Managerial Finance, vol. 26 no. 12
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 1 September 2000

Fred F. Farshad, James D. Garber and Juliet N. Lorde

A novel approach using artificial neural networks (ANNs) for predicting temperature profiles evaluated 27 wells in the Gulf of Mexico. Two artificial neural network models were…

1161

Abstract

A novel approach using artificial neural networks (ANNs) for predicting temperature profiles evaluated 27 wells in the Gulf of Mexico. Two artificial neural network models were developed that predict the temperature of the flowing fluid at any depth in flowing oil wells. Back propagation was used in training the networks. The networks were tested using measured temperature profiles from the 27 oil wells. Both neural network models successfully mapped the general temperature‐profile trends of naturally flowing oil wells. The highest accuracy was achieved with a mean absolute relative percentage error of 6.0 per cent. The accuracy of the proposed neural network models to predict the temperature profile is compared to that of existing correlations. Many correlations to predict temperature profiles of the wellbore fluid, for single‐phase or multiphase flow, in producing oil wells have been developed using theoretical principles such as energy, mass and momentum balances coupled with regression analysis. The Neural Network 2 model exhibited significantly lower mean absolute relative percentage error than other correlations. Furthermore, in order to test the accuracy of the neural network models to that of Kirkpatrick’s correlation, a mathematical model was developed for Kirkpatrick’s flowing temperature gradient chart.

Details

Engineering Computations, vol. 17 no. 6
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 1 May 2002

C.M. TAM, THOMAS K.L. TONG and SHARON L. TSE

This paper aims to develop a quantitative model for predicting the productivity of excavators using artificial neural networks (ANN), which is then compared with the multiple…

Abstract

This paper aims to develop a quantitative model for predicting the productivity of excavators using artificial neural networks (ANN), which is then compared with the multiple regression model developed by Edwards & Holt (2000). A neural network using the architecture of multilayer feedforward (MLFF) is used to model the productivity of excavators. Finally, the modelling methods, predictive behaviours and the advantages of each model are discussed. The results show that the ANN model is suitable for mapping the non‐linear relationship between excavation activities and the performance of excavators. It concludes that the ANN model is an ideal alternative for estimating the productivity of excavators.

Details

Engineering, Construction and Architectural Management, vol. 9 no. 5/6
Type: Research Article
ISSN: 0969-9988

Keywords

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