RBF Neural Network for Nonlinear System Approximation
- Login to Download
- 1 Credits
Resource Overview
Successful implementation of RBF neural network for approximating nonlinear systems, with discussions on current limitations and areas for improvement. Implementation includes radial basis function centers selection, weight optimization, and network training algorithms.
Detailed Documentation
RBF neural network serves as an effective method for approximating nonlinear systems, demonstrating successful practical implementation and achieving significant results in our experiments. The implementation typically involves selecting appropriate radial basis function centers using clustering algorithms like K-means, optimizing connection weights through least squares methods, and employing gradient descent for network training. However, we acknowledge certain limitations that require further refinement and enhancement, particularly in areas such as network structure optimization, parameter tuning sensitivity, and generalization capability. We welcome additional guidance and suggestions to further advance our research work, including improvements in activation function selection, training efficiency optimization, and handling of high-dimensional input spaces.
- Login to Download
- 1 Credits