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Related Experiment Videos

Neural-network prediction with noisy predictors.

¹Department of Mathematics, Northeastern University, Boston, MA 02115, USA.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This summary is machine-generated.

Neural network predictions can still be optimal with noisy inputs. Adjusting activation functions, not retraining networks, corrects for measurement errors, improving prediction accuracy.

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Area of Science:

Machine Learning
Artificial Intelligence
Statistics

Background:

Input variables in neural network predictions often contain measurement errors.
These errors arise when original variables are unavailable and replaced by predicted values.
Ignoring these errors leads to suboptimal prediction performance.

Purpose of the Study:

To demonstrate that optimal predictions with noisy inputs are achievable.
To show that neural network structure and weights can remain unchanged.
To provide a method for adjusting activation functions to correct for input errors.

Main Methods:

Deriving conditions under which optimal prediction with noisy inputs is possible.
Showing that the same neural network structure and weights can be used.
Developing an exact formula for adjusting activation functions for logistic networks with Gaussian errors.

Main Results:

Optimal prediction with noisy input variables can use the same neural network architecture and weights.
Only the activation functions require adjustment to account for measurement errors.
An explicit formula is provided for adjusting activation functions in logistic networks.

Conclusions:

Achieving optimal neural network predictions with noisy inputs is possible without costly retraining.
Adjusting activation functions is a viable method to correct for measurement errors.
The proposed approach is effective, as illustrated by short-term load forecasting applications.