Study on Construction Bidding Management Based on GA-BP Neural Networks

2014 ◽  
Vol 584-586 ◽  
pp. 2423-2426
Author(s):  
Tian Bao Wu ◽  
Xun Liu ◽  
Tai Quan Zhou
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Construction Project ◽  
Basic Theory ◽  
Slow Convergence ◽  
Artificial Neural

In the bidding evaluation, the deviations are likely to be brought about by experts' subjectivity, arbitrary and tendentiousness. A method for construction project bidding based on the BP neural network improved by GA (Genetic Algorithm) is proposed. On the basis of the basic theory of the BP neural network, discussions are provided on how to rectify the drawbacks of slow convergence and prone to convergence to minimum with the use of GA. The model is successfully applied GA - BP artificial neural networks to project, which are in concert with the result of experts. The study makes contribution to research about the evaluation system of construction bidding management.

Application of Magnetorheological Damper Semi-Active Control Based on Artificial Neural Networks

2010 ◽  
Vol 171-172 ◽  
pp. 654-658
Author(s):  
De Kun Yue ◽  
Qi Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
System Identification ◽  
Active Control ◽  
Bp Neural Network ◽  
Structural Response ◽  
Magnetorheological Damper ◽  
Building Structure ◽  
Artificial Neural

Uncertainty for the building structure and nonlinear, this simulation of a multi-storey structure under earthquake is presented based on the BP neural network and system identification, controller will be built to effectively reduce the structural response, and to strengthen the unique damper performance.

Aeronautical Study for Flight Time Estimation Applying Artificial Neural Network and Genetic Algorithm

2014 ◽  
Vol 919-921 ◽  
pp. 1063-1074
Author(s):  
Yung Ching Lin ◽  
Lee Kuo Lin ◽  
Shao Hong Tsai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Flight Time ◽  
Air Traffic ◽  
Artificial Neural

Since the adoption of open-air policy, people make more frequent use of air travel to do various business or tourism activities. The volume of air traffic has greatly increased, along with the occurrences of traffic jam in the air. Delays of landings or take-offs and the congestions in the approach air space have become commonplace, exacerbating the already heavy workload of air-traffic controllers and the inadequacies of ATC system. Therefore, a study of flight time in ATC operation to help alleviate airspace congestions has become more and more urgent and important. Taking international airway A1 as an example, this study makes use of the known entry time, flight altitude, speed, penetrating and descending as the input of artificial neural networks; the time between departure and transfer point as the output of Artificial Neural Networks, to establish artificial neural network. Applying artificial neural networks and genetic algorithm to the study to simulate the result of actual flight, one can precisely estimate the flight time, thereby making it an efficient air-traffic-control instrument. It can help controllers handle different time segments of air traffic, thus upgrading the quality of air traffic control service.

Based on Artificial Neural Network Simulation of Alloy Finite Element

2013 ◽  
Vol 710 ◽  
pp. 739-742
Author(s):  
Shu Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fem Analysis ◽  
Algorithm Optimization ◽  
Neural Network Simulation ◽  
Artificial Neurons ◽  
Artificial Neural

Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. First modal analyses of microstructure defects are performed in ANSYS. Second the genetic algorithm is implemented in MATLAB to Calculate the Value of b and p. The last, The FEM analysis results are imported in ANSYS about the Stress distribution. The result presented in this paper is obtained using the Genetic Algorithm Optimization Toolbox.

Study of Bidding Decision-Making in Construction Project Based on LM-BP Neural Networks

2014 ◽  
Vol 584-586 ◽  
pp. 2339-2342 ◽  
Author(s):  
Tian Bao Wu ◽  
Xun Liu ◽  
Tai Quan Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Bp Neural Network ◽  
Construction Project ◽  
Bp Neural Networks ◽  
Slow Convergence ◽  
Bidding Process

There is a tendency that arbitrary and tendentiousness are brought by experts’ subjectivity during the bidding evaluation. A method for construction project bidding based on the BP neural network improved by LM is proposed. To overcome the disadvantage of slow convergence and being prone to converge to minimum, LM was used to be combined with the BP neural networks. It has been proved by an application an effective complement for the deficiency of BP neural network on the bidding decision of construction project. Through the application in reality, we found that the LM-BP neural network in resolving the problems during the bidding process is meaningful.

Construction project control using artificial neural networks

1997 ◽  
Vol 11 (1) ◽  
pp. 45-57 ◽  
Author(s):  
H. Al-Tabtabai ◽  
N. Kartam ◽  
I. Flood ◽  
A.P. Alex
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control System ◽  
Construction Project ◽  
Project Control ◽  
Cost Variance ◽  
The Neural Network ◽  
Network Modules ◽  
Artificial Neural

AbstractArtificial neural networks are finding wide application to a variety of problems in civil engineering. This paper describes how artificial neural networks can be applied in the area of construction project control. A project control system capable of predicting and monitoring project performance (e.g., cost variance and schedule variance) based on observations made from the project environment is described. This project control system has five neural network modules that allow a project manager to automatically generate revised project plans at regular intervals during the progress of the project. These five modules are similar in design and implementation. Therefore, this paper will present the main issues involved in the development of one of these five neural network modules, that is, the module for identifying schedule variance. A description of a graphical user interface integrating the neural network modules developed with project management software, and a discussion on the power and limitations of the overall system conclude the paper.

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.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals Nonlinear Time-Series Analysis of Pulsation of Post-AGB Stars by Genetic Algorithm/Neural Network Hybrid Systems

2000 ◽  
Vol 176 ◽  
pp. 135-136
Author(s):  
Toshiki Aikawa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid System ◽  
Nonlinear Time Series ◽  
Photometric Data ◽  
Nonlinear Time Series Analysis ◽  
Agb Stars

AbstractSome pulsating post-AGB stars have been observed with an Automatic Photometry Telescope (APT) and a considerable amount of precise photometric data has been accumulated for these stars. The datasets, however, are still sparse, and this is a problem for applying nonlinear time series: for instance, modeling of attractors by the artificial neural networks (NN) to the datasets. We propose the optimization of data interpolations with the genetic algorithm (GA) and the hybrid system combined with NN. We apply this system to the Mackey–Glass equation, and attempt an analysis of the photometric data of post-AGB variables.

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