Applying neural network and scatter search to optimize parameter design with dynamic characteristics

Parameter design is critical to enhancing a system's robustness by identifying specific control factor set points (levels) that make the system least sensitive to noise. Engineers have conventionally applied Taguchi methods to optimize parameter design. However, Taguchi methods can only obtain the optimal solution among the specified control factor levels. They cannot identify the real optimum when the parameter values are continuous. This study proposes a hybrid procedure combining neural networks and scatter search to optimize the continuous parameter design problem. First, neural networks are used to simulate the relationship between the control factor values and corresponding responses. Second, scatter search is employed to obtain the optimal parameter settings. The desirability function is utilized to transform the multiple responses into a single response. A case with dynamic characteristics is carried out in blood glucose strip manufacturing in Taiwan to demonstrate the practicability of the proposed procedure.

[1]  Henry W. Altland,et al.  Engineering Methods for Robust Product Design , 1996 .

[2]  George Chryssolouris,et al.  A Comparison of Statistical and AI Approaches to the Selection of Process Parameters in Intelligent Machining , 1990 .

[3]  Fred W. Glover,et al.  A Template for Scatter Search and Path Relinking , 1997, Artificial Evolution.

[4]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[5]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[6]  Jen-Der Lin,et al.  Comparing Self-Monitoring Blood Glucose Devices , 1994 .

[7]  F. Glover.  Scatter search and path relinking , 1999 .

[8]  F. Glover,et al.  Fundamentals of Scatter Search and Path Relinking , 2000 .

[9]  F Vinicor,et al.  Diabetes--a common, growing, serious, costly, and potentially preventable public health problem. , 2000, Diabetes research and clinical practice.

[10]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[11]  Chao-Ton Su,et al.  Optimization of parameter design: An intelligent approach using neural network and simulated annealing , 2000, Int. J. Syst. Sci..

[12]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[13]  Kwang-Jae Kim,et al.  Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions , 2000 .

[14]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[15]  Chao-Ton Su,et al.  Optimal design for a ball grid array wire bonding process using a neuro-genetic approach , 2002 .

[16]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[17]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[18]  G. Geoffrey Vining,et al.  Combining Taguchi and Response Surface Philosophies: A Dual Response Approach , 1990 .