Multi-response Problem with Adaptive Weight Consideration by Using Weight Aggregator and ANNs
This study proposes a procedure incorporating artificial neural networks technique with adaptive weight consideration to address parameter optimization of a multiple responses problem. No matter what type of the experimental designs being employed, the proposed approach can be directly employed. Besides, the consistency and difference among several evaluations among those multiple responses can also be studied via the designed aggregation weight values in our proposed procedure. An illustrative example owing to the lead frame manufacturer in Taiwan is also employed to demonstrate the feasibility and rationality of the proposed procedure.
multi-response problem weight aggregator parameter optimization lead frame back-propagation neural network (bpnn)