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Article
Publication date: 1 March 1997

Margarita M. Lenk, Elaine M. Worzala and Ana Silva

Compares the predictive performance of artificial neural networks to hedonic pricing models, a more traditional valuation tool. The results document similar predictive…

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1358

Abstract

Compares the predictive performance of artificial neural networks to hedonic pricing models, a more traditional valuation tool. The results document similar predictive performance evidenced from both techniques, which contradicts some of the earlier studies which support a position of artificial neural network superiority. Demonstrates that at least 18 per cent of the “normal” property predictions and over 70 per cent of the “outlier” property predictions contained valuation errors greater than 15 per cent of the actual sales price. The combination of these substantial errors and the model‐optimization costs incurred motivate a message of caution before artificial neural networks are adopted by the real estate valuation and/or lending industries.

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Journal of Property Valuation and Investment, vol. 15 no. 1
Type: Research Article
ISSN: 0960-2712

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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…

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.

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

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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…

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

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Article
Publication date: 17 July 2007

Hassan Al Nageim, Ravindra Nagar and Paulo J.G. Lisboa

To investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings.

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1588

Abstract

Purpose

To investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings.

Design/methodology/approach

Database of 234 design examples has been developed using commercially available detailed design software. These examples represent building up to 20 storeys. Feed forward back‐propagation neural network is trained on these examples. The results obtained from the artificial neural network are evaluated by re‐substitution, hold‐out and ten‐fold cross‐validation techniques.

Findings

Results indicate that artificial neural network would give a performance of 97.91 percent (ten‐fold cross‐validation). The performance of this system is benchmarked by developing a binary logistic regression model from the same data. Performance of the two models has been compared using McNemar's test and receiver operation characteristics curves. Artificial neural network shows a better performance. The difference is found to be statically significant.

Research limitations/implications

The developed model is applicable only to steel building up to 20 storeys. The feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings more than 20 storeys has not been investigated.

Practical implications

Implementation of the broad methodology outlined for the use of neural networks can be accomplished by conducting short training courses. This will provide personnel with flexibility in addressing buildings‐specifics bracing conditions and limitations.

Originality/value

In tall building design a lot of progress has been made in the development of software tools for numerical intensive tasks of analysis, design and optimization, however, professional software tools are not available to help the designer to choose an optimum building configuration at the conceptual design stage. The presented research provides a methodology to investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall buildings. It is found that this approach for the selection of bracings in tall buildings is a better and cost effective option compared with database generated on the basis of expert opinion. It also correctly classifies and recommends the type of trussed bracing system.

Details

Construction Innovation, vol. 7 no. 3
Type: Research Article
ISSN: 1471-4175

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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…

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

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Article
Publication date: 28 October 2021

Tahmineh Aldaghi and Shima Javanmard

This paper aims to evaluate the performance of the Mashhad No. 5 wastewater treatment plant (WWTP) using a combination of data mining (regression) algorithms and artificial

Abstract

Purpose

This paper aims to evaluate the performance of the Mashhad No. 5 wastewater treatment plant (WWTP) using a combination of data mining (regression) algorithms and artificial neural networks.

Design/methodology/approach

In this research, the performance of WWTP located in Mashhad, Iran, has been evaluated using two data mining models, neural network and regression model.

Findings

The proposed model has the potential of implementing in other WWTPs in Iran or other countries.

Originality/value

The authors would also like to thank Mashhad No.5 WWTP for data access.

Details

Journal of Engineering, Design and Technology , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1726-0531

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Article
Publication date: 1 May 1994

Rebecca Chung‐Fern Wu

Investigates the possibility of applying artificial intelligence tosolve practical auditing problems faced by the public sector, namely thetax auditor of the Internal…

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757

Abstract

Investigates the possibility of applying artificial intelligence to solve practical auditing problems faced by the public sector, namely the tax auditor of the Internal Revenue Services, when targeting firms for further investigation. Suggests that organizations which incorporate an operational artificial neural network system will raise their performance greatly. Proposes that the neural network will overcome problems faced by a direct knowledge acquisition method in building an expert system to preserve the expertise of senior auditors by the IRS in Taiwan. Provides an explanation of the neural network theory with regard to multi‐ and single‐layered neural networks. Statistics reveal the neural network performs favourably, and that three‐layer networks perform better than two‐layer neural networks. The results strongly suggest that neural networks can be used to identify firms requiring further auditing investigation, and also suggest future implications for intelligent auditing machines.

Details

Managerial Auditing Journal, vol. 9 no. 3
Type: Research Article
ISSN: 0268-6902

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Article
Publication date: 1 February 2005

Seda Özmutlu and Fatih Çavdur

This study aims to propose an artificial neural network to identify automatically topic changes in a user session by using the statistical characteristics of queries, such…

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1054

Abstract

Purpose

This study aims to propose an artificial neural network to identify automatically topic changes in a user session by using the statistical characteristics of queries, such as time intervals and query reformulation patterns.

Design/methodology/approach

A sample data log from the Norwegian search engine FAST (currently owned by Overture) is selected to train the neural network and then the neural network is used to identify topic changes in the data log.

Findings

A total of 98.4 percent of topic shifts and 86.6 percent of topic continuations were estimated correctly.

Originality/value

Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms for search engines, which can offer custom‐tailored services to the web user. Identification of topic changes within a user search session is a key issue in the content analysis of search engine user queries.

Details

Online Information Review, vol. 29 no. 1
Type: Research Article
ISSN: 1468-4527

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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…

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1099

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

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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

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

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