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Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

¹Department of Business Administration, Chung Yuan Christian University, Chung Li 32023, Taiwan.

Thescientificworldjournal

|August 12, 2014

Summary

This summary is machine-generated.

This study introduces a robust nonlinear interval regression model using a genetic algorithm and multilayer perceptron. The method effectively handles uncertain data with outliers, improving the accuracy of computational intelligence models.

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Area of Science:

Computational intelligence
Machine learning
Data science

Background:

Fuzzy regression and neural networks are used for nonlinear interval regression with imprecise data.
Standard models perform poorly when training data contain outliers.
Robust algorithms are needed to resist outliers in interval regression analysis.

Purpose of the Study:

To develop a robust nonlinear interval regression model resistant to outliers.
To address the challenge of prespecifying outlier contamination levels.
To improve the performance of computational intelligence models with contaminated datasets.

Main Methods:

Utilized multilayer perceptron for model construction.
Employed a genetic algorithm for robust model training.
Designed the model to minimize the impact of outliers on data intervals.

Main Results:

The proposed model demonstrates robustness against outliers in training data.
Outliers have a minimal effect on the determination of the data interval.
Simulation results confirm the model's effectiveness on contaminated datasets.

Conclusions:

The genetic algorithm-based multilayer perceptron model provides a robust solution for nonlinear interval regression.
This approach enhances the reliability of computational intelligence models in real-world scenarios with imperfect data.
The method offers a practical way to manage data uncertainty and outliers in regression analysis.