Nonlinear System Identification using Spatio-Temporal RBF-NN

버전 1.1.2 (357 KB) 작성자: Shujaat Khan
In this submission, I implemented RBF, Fractional RBF, and Spatio-Temporal RBF Neural Network for nonlinear system identification task
다운로드 수: 703
업데이트 날짜: 2018/12/5

라이선스 보기

Herein, you will find three variants of radial basis function neural network (RBF-NN) for nonlinear system identification task. In particular, I implemented RBF with conventional and fractional gradient descent, and compared the performance with spatio-temporal RBF-NN.

* For citations see [cite as] section

인용 양식

Shujaat Khan (2024). Nonlinear System Identification using Spatio-Temporal RBF-NN (https://www.mathworks.com/matlabcentral/fileexchange/68415-nonlinear-system-identification-using-spatio-temporal-rbf-nn), MATLAB Central File Exchange. 검색됨 .

Khan, Shujaat, et al. “A Novel Adaptive Kernel for the RBF Neural Networks.” Circuits, Systems, and Signal Processing, vol. 36, no. 4, Springer Nature, July 2016, pp. 1639–53, doi:10.1007/s00034-016-0375-7.

양식 더 보기

Khan, Shujaat, et al. “A Fractional Gradient Descent-Based RBF Neural Network.” Circuits, Systems, and Signal Processing, vol. 37, no. 12, Springer Nature America, Inc, May 2018, pp. 5311–32, doi:10.1007/s00034-018-0835-3.

양식 더 보기

Khan, Shujaat, et al. “Spatio-Temporal RBF Neural Networks.” 2018 3rd {IEEE} International Conference on Emerging Trends in Engineering, Sciences and Technology ({ICEEST}), {IEEE}, 2018

MATLAB 릴리스 호환 정보
개발 환경: R2015a
모든 릴리스와 호환
플랫폼 호환성
Windows macOS Linux
카테고리
Help CenterMATLAB Answers에서 Sequence and Numeric Feature Data Workflows에 대해 자세히 알아보기

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
버전 게시됨 릴리스 정보
1.1.2

- update citation information

1.1.1

- title change

1.1

- Comparison with conventional and fractional variant

1.0.2

- Simplification of code syntax

1.0.1

- Example added

1.0.0