Table 10 DFNN model performance in Q3 with K-fold.

From: Path-based evaluation of deep learning models for solving inverse kinematics in a revolute-prismatic robot

Configuration

Architecture (hidden layers × units)

Batch size

L2 regularization

Epoch

Validation loss

Single-output (\(\:\theta\:\))

3 × 128

32

0.0005

150

0.067

Single-output (\(\:d\))

0.053

Dual-output (\(\:\theta\:,\:d\))

3 × 128

0.2494

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