Name
Abstract | ||
|---|---|---|
This paper proposes an extreme learning machine approach for performing the blood glucose system estimation. The extreme learning machine consists of a unitary matrix in the first layer, a set of nonlinear activation functions and a weight vector in the second layer. Here, the radical basis functions are used as the nonlinear activation functions. The joint design problem of these three sets of parameters are a nonconvex optimization problem. An iterative algorithm based on the joint genetic algorithm and the linear programming is applied to find a near global optimal solution of the nonconvex optimization problem. The simulation results show that the proposed method is effective for performing the blood glucose estimation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CSNDSP.2018.8471836 | 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP) |
Keywords | Field | DocType |
extreme learning approach,blood glucose estimation,extreme learning machine approach,blood glucose system estimation,unitary matrix,nonlinear activation functions,weight vector,radical basis functions,nonconvex optimization problem,global optimal solution,genetic algorithm,iterative algorithm,linear programming | Mathematical optimization,Nonlinear system,Extreme learning machine,Computer science,Iterative method,Unitary matrix,Linear programming,Basis function,Optimization problem,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
978-1-5386-1336-8 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuwei Liu | 1 | 0 | 0.34 |
| Wing-Kuen Ling | 2 | 9 | 4.21 |
| Chi-Kong Li | 3 | 0 | 0.34 |
| Sam Ho | 4 | 0 | 0.34 |