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Identification of an Experimental Process by B-spline Neural Network Using Improved Differential Evolution Training
| Content Provider | Semantic Scholar |
|---|---|
| Author | Coelho, Leandro Dos Santos Guerra, Fabio Alessandro |
| Copyright Year | 2006 |
| Abstract | B-spline neural network (BSNN), a type of basis function neural network, is trained by gradient-based methods, which may fall into local minimum during the learning procedure. To overcome the problems encountered by the conventional learning methods, differential evolution (DE) an evolutionary computation methodology can provide a stochastic search to adjust the control points of a BSNN are proposed. DE incorporates an efficient way of self-adapting mutation using small populations. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution and robustness. In this paper, we propose a modified DE using chaotic sequence based on logistic map to train a BSNN. The numerical results presented here indicate that the chaotic DE is effective in building a good BSNN model for nonlinear identification of an experimental nonlinear yo-yo motion control system. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cs.armstrong.edu/wsc11/pdf/pap156s2-file1.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Artificial neural network B-spline Basis function Benchmark (computing) Biological Evolution Biological Neural Networks Control system Convergence (action) Differential evolution Evolutionary computation Gradient Logistic map Mathematical optimization Maxima and minima Mutation Nonlinear system Numerical analysis Paraneoplastic Cerebellar Degeneration, Anti-Yo-Associated Population Simulation Stochastic optimization |
| Content Type | Text |
| Resource Type | Article |
