Artificial Neural Network and Genetic Algorithms for Discontinuously Reinforced Aluminum Composites Frictional Behaviour Prediction

2005 ◽  
Author(s):  
Ming Qiu ◽  
Yong-Zhen Zhang ◽  
Jun Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Working Conditions ◽  
Composite Component ◽  
Network Construction ◽  
Predictive Quality ◽  
Modeling Data ◽  
Model Of Friction ◽  
Frictional Behaviour

By using genetic algorithms and radius basis function (GARBF) neural network, the predicting model of friction coefficient has been established based on a measured database with five sliding velocities of 40, 55, 70, 85, 100 m/s and four different normal pressures of 0.1333, 0.4667, 0.60 and 0.7333 MPa. The modeling results confirm the feasibility of the GARBF network and its good correlation with the experimental results. The predictive quality of the GARBF network can be further improved by enlarging the training datasets and by optimizing the network construction. A well-trained GARBF modeling is expected to be very helpful for selecting composite component under different working conditions, and for predicting tribological properties. Finally, by using GARBF modeling data to predict analysis, the results show that the friction coefficients of these composites were increased with the increase in material thermal capability at some region.

PREDICTING SOFTWARE QUALITY, DURING TESTING, USING NEURAL NETWORK MODELS: A COMPARATIVE STUDY

1994 ◽  
Vol 01 (03) ◽  
pp. 303-319 ◽  
Author(s):  
TAGHI M. KHOSHGOFTAAR ◽  
ROBERT M. SZABO
Keyword(s):  
Neural Network ◽  
Software Quality ◽  
Principal Components ◽  
Network Modeling ◽  
Network Models ◽  
Neural Nets ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Predictive Quality

The application of statistical modeling techniques has been an intensely pursued area of research in the field of software engineering. The goal has been to model software quality and use that information to better understand the software development process. Neural network modeling methods have recently been applied to this field. The results reported indicate that neural network models have better predictive quality than some statistical models when predicting reliability and the number of faults. In this paper, we will investigate the application of principal components analysis to neural network modeling as a way of improving the predictive quality of neural network quality models. Using data we collected from a large commercial software system, we developed a multiple regression model using the principal components. Then, we trained two neural nets, one with raw data, and one with principal components. Then, we compare the predictive quality of the three competing models for a variety of quality measures.

scholarly journals An Improved Genetic Algorithm and Neural Network-Based Evaluation Model of Classroom Teaching Quality in Colleges and Universities

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Huaying Zhang ◽  
Bin Xiao ◽  
Jinqiong Li ◽  
Min Hou
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Colleges And Universities ◽  
University Teaching ◽  
Classroom Teaching ◽  
Adaptive Mutation ◽  
Improved Genetic Algorithm

Research on educational quality has gotten a lot of attention as the current higher education teaching reform continues to deepen and grow. The key to improving education quality is to improve teaching quality, and teacher evaluation is an important tool for doing so. As a result, educational management requires the development and refinement of a system for evaluating teaching quality. Traditional approaches to assessing teaching quality, on the other hand, are problematic due to their limitations. As a result, a scientific and reasonable model for evaluating the teaching quality of college undergraduate teachers must be developed. We present a unique model for evaluating the quality of classroom teaching in colleges and universities, which is based on improved genetic algorithms and neural networks. The basic idea is to use adaptive mutation genetic algorithms to refine the initial weights and thresholds of the BP neural network. The teaching quality evaluation findings were improved by improving the neural network’s prediction accuracy and convergence speed, resulting in a more practical scheme for evaluating college and university teaching quality. We have conducted simulation experiments and comparative analysis, and the mean square error of the results of the proposed model is very low, which proves the effectiveness and superiority of the algorithm.

Prediction of the Consistency of Concrete by Means of the Use of Artificial Neural Networks

2011 ◽  
pp. 1484-1493
Author(s):  
Bele´n Gonzalez ◽  
Ma Isabel Martinez ◽  
Diego Carro
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Fresh Concrete ◽  
Rheological Behaviour ◽  
Simple Approach ◽  
Artificial Neural

This chapter displays an example of application of the ANN in civil engineering. Concretely, it is applied to the prediction of the consistency of the fresh concrete through the results that slump test provides, a simple approach to the rheological behaviour of the mixtures. From the previously done tests, an artificial neural network trained by means of genetic algorithms adjusts to the situation, and has the variable value of the cone as an output, and as an input, diverse variables related to the composition of each type of concrete. The final discussion is based on the quality of the results and its possible application.

Prediction of the Consistency of Concrete by Means of the Use of Artificial Neural Networks

2006 ◽  
pp. 188-201
Author(s):  
Belén Gonzalez ◽  
M. Isabel Martinez ◽  
Diego Carro
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Fresh Concrete ◽  
Rheological Behaviour ◽  
Simple Approach ◽  
Artificial Neural

This chapter displays an example of application of the ANN in civil engineering. Concretely, it is applied to the prediction of the consistency of the fresh concrete through the results that slump test provides, a simple approach to the rheological behaviour of the mixtures. From the previously done tests, an artificial neural network trained by means of genetic algorithms adjusts to the situation, and has the variable value of the cone as an output, and as an input, diverse variables related to the composition of each type of concrete. The final discussion is based on the quality of the results and its possible application.

scholarly journals Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer

2021 ◽  
Author(s):  
Hasan Tercan ◽  
Philipp Deibert ◽  
Tobias Meisen
Keyword(s):  
Neural Network ◽  
Manufacturing Companies ◽  
Process Data ◽  
Regression Problem ◽  
Predictive Quality ◽  
The Neural Network ◽  
And Performance ◽  
Data Efficiency ◽  
Continual Learning

AbstractDeep learning-based predictive quality enables manufacturing companies to make data-driven predictions of the quality of a produced product based on process data. A central challenge is that production processes are subject to continuous changes such as the manufacturing of new products, with the result that previously trained models may no longer perform well in the process. In this paper, we address this problem and propose a method for continual learning in such predictive quality scenarios. We therefore adapt and extend the memory-aware synapses approach to train an artificial neural network across different product variations. Our evaluation in a real-world regression problem in injection molding shows that the approach successfully prevents the neural network from forgetting of previous tasks and improves the training efficiency for new tasks. Moreover, by extending the approach with the transfer of network weights from similar previous tasks, we significantly improve its data efficiency and performance on sparse data. Our code is publicly available to reproduce our results and build upon them.

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

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