SlideServe
Télécharger Obtenir Pro
Menu
Accueil Pro
Préférences
ApparenceLight Aide et commentaires
© SlideServe · v2026

Car Manufacturing through ANN Models: Predicting Success Factors

ghalib avatar
ghalib
12 diapositives · 308 Views · Oct 13, 2012

Application of Artificial Neural Networks (ANN) in predicting car success based on key features. By analyzing car buying processes, consumer expectations, and reviews from various sources, the study identifies critical design elements and performance metrics that influence rankings and market success. Algorithms such as Multi-layered Perceptron and K-Nearest Neighbor were implemented on data from the UC-Irvine repository. Results show a promising classification success rate and highlight the role of feature scaling and configuration in improving prediction accuracy.

Car Manufacturing through ANN Models: Predicting Success Factors - slide 1
01 / 12Car Manufacturing through ANN Models: Predicting Success Factors
1 / 17

Télécharger la présentation

Car Manufacturing through ANN Models: Predicting Success Factors
Une image / un lien ci-dessous est fourni (tel quel) pour télécharger la présentation

Politique de téléchargement : le contenu du site vous est fourni TEL QUEL pour votre information et votre usage personnel ; il ne peut être vendu, concédé sous licence ni partagé sur d’autres sites sans l’accord de son auteur.Le contenu vous est fourni TEL QUEL, pour votre information et votre usage personnel uniquement. Téléchargez la présentation en cliquant sur ce lien. Lors du téléchargement, si vous ne parvenez pas à télécharger une présentation, l’éditeur a peut-être supprimé le fichier de son serveur.Pendant le téléchargement, si vous ne parvenez pas à obtenir une présentation, le fichier a peut-être été supprimé par l’éditeur.

F I N

Plus de contenu associé

Transcription de la présentation

  1. ECE 539 Final Project ANN approach to help manufacturing of a better car Prabhdeep Singh Virk Fall 2010

  2. Car buying process • Read reviews, consumer reports from various news agencies. • Consider rankings provided by US News, JD Power etc. • Ask colleagues and friends for recommendation.

  3. Good car – owner’s perspective • Exterior and interior design? • Features like acceleration, speed, fuel economy etc? • Safety features ? • Reliability ? • Overall Price?

  4. How to make Good car ? • Need to know what features are making it a good car. • Predict what are car consumers want and expectations? • Possible features/design responsible for high ranking. • Changes/improvements that can affect the overall ranking of car.

  5. When expectation don’t match? • Car company loose customers due to lack of interest in their product. • Decline in sales cause catastrophic effects in terms of loosing jobs and revenue and effecting economy. • In fact failing to innovate and declining sales over past decade was two major cause of automotive industry crisis. • In this project I try to implement reverse mapping of accurately predicting the car success based on features using ANN. • Ann algorithms are proven very successful in pattern classification based problems.

  6. Pattern Classification using ANN • Car Evaluation data from UC-Irvine data repository. • 6 Car features • Price Over all. • Buying price. • Maintenance price • Technical characteristics • # of doors • Capacity • Luggage boot size • Safety • 4 output classes. • Unacceptable (1210 ) • Acceptable ( 384 ) • Good ( 69 ) • Very good. ( 65 ) • Algorithms tested: • K Nearest Neighbors • Multi-layered Precptron

  7. K Nearest Neighbor implementation. • Tested with 1 – 15 neighbors • Increasing # of neighbors have adverse effect.

  8. Multi-layered Precptron implementation. Data Pre-processing: • Scaling input features on [-5,5] scale. • Random train/test datasets, with fixed minimum samples(10) / class. MLP configuration: • Epochs = 1000 • Learning rate = 0.05 • Momentum = 0.8 • # of hidden layers = 2 • # of neurons/ hidden layer = 6 • Steepest Descent Gradient.

  9. Results after 10 iterations: • Success Rate (%)= 90.5093 - 95.6019 • Mean success rate(%) = 92.4306 • Standard Deviation(%)= 1.4355 • Resultant Confusion Matrix: • 294 7 1 0 • 5 87 5 1 • 0 4 12 2 • 0 3 1 10

  10. Error reflecting role of learning rate and momentum.

  11. Conclusion • K-Nearest neighbor is ineffective due to the difference in class distribution. • MLP performed well, as long as it is trained with at least 10 samples of each class. • Feature scaling improves classification rate. • Classification rate improves with increase in neurons. • Momentum helps converging faster. • High learning rate >0.5 case Error to oscillate. • Its possible to predict a car success ranking based on the features available.

  12. Questions? Thank you