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The time required for the learning process of neural networks depends on the number of total weights and that of the input-output pairs in the training set. In the proposed learning process, after the learning is progressed, e.g., 1000 iterations, input-output pairs having had worse errors are extracted from the original training set and form a new temporary set. From the next iteration, the temporary set is applied instead of the original set. This means that only pairs with worse errors are used for updating the weights until the mean value of errors decreases to a level. After the learning using the temporary set, the original set is applied again instead of the temporary set. By alternately applying the two types of sets for learning that the calculation load for convergence can be efficiently reduced. The effectiveness of the proposed method is proved by applying to an inverse kinematics problem of an industrial robot.

Neural Network-Based Inverse Kinematics for an Industrial Robot and Its Learning Method