Estimating a civil aircraft’s development cost with a GM(1, N) model and an MLP neural network

2017 ◽  
Vol 7 (1) ◽  
pp. 2-18 ◽  
Author(s):  
Nai-ming Xie ◽  
Song-Ming Yin ◽  
Chuan-Zhen Hu
Keyword(s):  
Neural Network ◽  
Cost Estimation ◽  
Development Cost ◽  
Combined Approach ◽  
Limited Data ◽  
New Approach ◽  
Content Type ◽  
Development Costs ◽  
Civil Aircraft ◽  
New Type

Purpose The purpose of this paper is to study a new approach by combining a multilayer perceptron neural network (MLPNN) algorithm with a GM(1, N) model in order to estimate the development cost of a new type of aircraft. Design/methodology/approach First, data about developing costs and their influencing factors were collected for several types of Boeing and Airbus aircraft. Second, a GM(1, N) model was constructed to simulate development costs for a civil aircraft. Then, an MLPNN algorithm was added to optimize and revise the simulative and forecasting values. Finally, a combined approach, using both a GM(1, N) model and an MLPNN algorithm was adopted to forecast development costs for new civil aircraft. Findings The results show that the proposed approach could do the work of cost estimation for new types of aircraft. Rather than using a single model, the combined approach could improve simulative and forecasting accuracy. Practical implications Scientific cost estimation could improve management efficiency and promote the success of a new type of civil aircraft development. Considering that China’s civil aircraft research and development is at its very beginning stages, only very limited data could be collected. The development costs for civil aircraft are affected by a series of factors. The approach outlined by this paper could be applied to development cost estimations in China’s civil aircraft industry. Originality/value The paper has succeeded by constructing a cost estimation index system and proposing a novel combined cost estimation approach comprised of a GM(1, N) model and an MLPNN. It has undoubtedly contributed to improving the accuracy of cost estimations.

Estimating civil aircraft’s research and manufacture cost by using grey system model and neural network algorithm

2015 ◽  
Vol 5 (1) ◽  
pp. 89-104 ◽  
Author(s):  
Naiming Xie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Relational Model ◽  
Content Type ◽  
Network Algorithm ◽  
Parameters Selection ◽  
Civil Aircraft ◽  
Neural Network Algorithm ◽  
Grey Relational

Purpose – The purpose of this paper is to propose novel civil aircraft cost parameters’ selection method and novel cost estimation approach for civil aircraft so as to effectively simulate or forecast civil aircraft cost under poor information and small sample. Design/methodology/approach – Based on existent cost estimation indexes, this paper summarized civil aircraft research and manufacturing cost impact index system and adopted grey relational model to select most important impact factors. Consider civil aircrafts’ cost information could not be easily collected, the author must estimate their costs with limited sample and poor information. A combination model of GM (0, N) model and BP neural network algorithm is proposed. Both advantages of simulation of BP neural network algorithm and poor information generation of GM (0, N) were effectively combined. Then steps of combined model were given out. Finally, nine types of aircrafts were used to test the validity of proposed model. As comparing with the traditional multiple linear regression model and simple GM (0, N) model, results indicated that proposed model can do the work better. Findings – Grey relational model can be applied for parameters’ selection and combined GM (0, N) model and BP neural network algorithm can estimate aircraft’s cost as well. Results show that novel combined model could get high forecasting accuracy. Practical implications – Cost estimation is key problem in production management of civil aircraft. Effective cost management could promote competitiveness of aircraft manufacturing company. Proposed combined model can be applied for civil aircraft cost estimation. Similarly, it could be applied for other complex equipment cost estimation. Originality/value – The paper succeeds in proposing grey relational model for cost parameters’ selection and constructing a combination model of GM (0, N) model and BP neural network algorithm. Algorithm of the proposed model was discussed and steps were given out.

scholarly journals Construction tender price estimation standardization (TPES) in Malaysia

2018 ◽  
Vol 25 (3) ◽  
pp. 443-457 ◽  
Author(s):  
Salihudin Hassim ◽  
Ratnasamy Muniandy ◽  
Aidi Hizami Alias ◽  
Pedram Abdullah
Keyword(s):  
Neural Network ◽  
Coming Out ◽  
Cost Estimation ◽  
Influential Factors ◽  
Estimation Accuracy ◽  
Content Type ◽  
Estimation Process ◽  
Price Estimation ◽  
Fuzzy Neural ◽  
Artificial Neural Network Method

Purpose The pre-tender estimation process is still a hazy and inaccurate process, despite it has been practiced over decades, especially in Malaysia. The methods evolved over time largely depend on the amount of information available at the time of estimation. More often than not, the estimate produced during the pre-tender stage is far more than the tender cost of the project and sometimes, it is perilously underestimated and caused major problems to the client in the monetary planning. The purpose of this paper is to determine the most influential factors on the deviation of pre-tender cost estimation in Malaysia by conducting a survey. Design/methodology/approach Fuzzy logic, combined with artificial neural network method (fuzzy neural network) was then used to develop an estimating model to aid the pre-tender estimation process. Findings The results showed that the model is able to shift the cost estimation toward accuracy. This model can be used to improve the pre-tender estimation accuracy, enabling the client to take the necessary early measures in preparing the funding for a building project in Malaysia. Originality/value To the authors’ knowledge, this is the first study on tender price estimation standardization for a construction project in Malaysia. In addition, the authors have used factors from literature for the model, which shows the thoroughness of the developed model. Thus, the findings and the model developed in this study should be able to assist contractors in coming out with a more accurate tender price estimation.

Key indexes choosing approach of complex equipment’s development cost based on grey incidence cluster model

2016 ◽  
Vol 6 (1) ◽  
pp. 110-123 ◽  
Author(s):  
Naiming Xie ◽  
Chuanzhen Hu ◽  
Songming Yin
Keyword(s):  
Cost Estimation ◽  
Grey System Theory ◽  
Critical Parameters ◽  
Estimation Accuracy ◽  
Content Type ◽  
Complex Products ◽  
Civil Aircraft ◽  
Complex Equipment ◽  
Key Indicators ◽  
The Cost

Purpose – The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc. Design/methodology/approach – Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree. Findings – The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved. Practical implications – The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model. Originality/value – In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.

Estimation of construction project building cost by back-propagation neural network

2019 ◽  
Vol 18 (3) ◽  
pp. 601-609
Author(s):  
Qinghua Jiang
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Back Propagation ◽  
Estimation Accuracy ◽  
Average Error ◽  
Estimation Model ◽  
Content Type ◽  
Building Cost

Purpose Building cost is an important part of construction projects, and its correct estimation has important guiding significance for the follow-up decision-making of construction units. Design/methodology/approach This study focused on the application of back-propagation (BP) neural network in the estimation of building cost. First, the influencing factors of building cost were analyzed. Six factors were selected as input of the estimation model. Then, a BP neural network estimation model was established and trained by ten samples. Findings According to the experimental results, it was found that the estimation model converged at about 85 times; compared with radial basis function (RBF), the estimation accuracy of the model was higher, and the average error was 5.54 per cent, showing a good reliability in cost estimation. Originality/value The results of this study provide a reliable basis for investment decision-making in the construction industry and also contribute to the further application of BP neural network in cost estimation.

A novel GREY‒ASMAA model for reliability growth evaluation in the large civil aircraft test flight phase

2019 ◽  
Vol 10 (1) ◽  
pp. 46-55
Author(s):  
Sifeng Liu ◽  
Wei Tang ◽  
Dejin Song ◽  
Zhigeng Fang ◽  
Wenfeng Yuan
Keyword(s):  
Test Data ◽  
Grey Model ◽  
Reliability Growth ◽  
Limited Data ◽  
Content Type ◽  
Flight Phase ◽  
Virtual Test ◽  
Civil Aircraft ◽  
Test Flight ◽  
Growth Evaluation

Purpose The purpose of this paper is to present a novel GREY‒ASMAA model for reliability growth evaluation in the large civil aircraft test flight phase. Design/methodology/approach As limited data are collected during the large civil aircraft test flight phase, which are not enough to meet the requirements of the ASMAA model for reliability growth, four basic GM(1, 1) models, even grey model, original difference grey model, even difference grey model and discrete grey model, are presented. Then both forward and backward grey models GM(1,1) are built to forecast and obtain virtual test data on left and right sides. Then the ASMAA model for reliability growth evaluation can be built based on original and virtual test data. Findings Aiming at the background of poor information data during the large civil aircraft test flight phase, first, a novel GREY‒ASMAA model, which was combined by the grey model GM(1,1) with the ASMAA model, has been put forward in this paper. Practical implications The GREY‒ASMAA model for reliability growth evaluation can be used to solve the problem of reliability growth evaluation with poor information data during the large civil aircraft test flight phase, and it has been used in reliability evaluation of C919 at the test flight stage. Originality/value This paper presents two new definitions of forward grey model GM(1,1) and backward grey model GM(1,1), as well as a novel GREY‒ASMAA model for reliability growth evaluation of large civil aircraft during test flight phase.

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