Name
Abstract | ||
|---|---|---|
Fitting curve is a critical problem in many testing equipment and detection system. But there was larger relative error in fitting curve when the independent variable was relatively small. In this paper, Fitting curve is formulated to a constrained linear programming. a neural dynamics optimization algorithm is obtained by considering the problem in its dual space, and then the dynamic neural network is designed to solve the optimization problem recurrently. The experimental results show that the polynomial coefficients solved by the method is stable, compared with the least square method, the relative error is obviously reduced; The method is simple and requires less samples. It provides a new simple and accurate method of curve fitting for the quantitative detection. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-48490-7_3 | GENETIC AND EVOLUTIONARY COMPUTING |
Keywords | Field | DocType |
Fitting curve,Relative error,Neural dynamics optimization algorithm,The quantitative detection | Least squares,Mathematical optimization,Curve fitting,Computer science,Dual space,Algorithm,Optimization algorithm,Linear programming,Variables,Optimization problem,Approximation error | Conference |
Volume | ISSN | Citations |
536 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baoping Xiong | 1 | 0 | 2.70 |
| Zhenhua Gan | 2 | 0 | 0.68 |
| Fumin Zou | 3 | 3 | 7.16 |
| Yuemin Gao | 4 | 0 | 0.68 |
| Min Du | 5 | 50 | 11.13 |