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

팔로우

5.0

(1)

다운로드 수: 790

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 (2026). Nonlinear System Identification using Spatio-Temporal RBF-NN (https://kr.mathworks.com/matlabcentral/fileexchange/68415-nonlinear-system-identification-using-spatio-temporal-rbf-nn), MATLAB Central File Exchange. 검색 날짜: 2026/2/1.

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

태그 태그 추가

도움

도움 받은 파일: Nonlinear System Identification using RBF Neural Network

Nonlinear_System_Identification

버전	게시됨	릴리스 정보
1.1.2	2018/12/5	- update citation information	다운로드
1.1.1	2018/11/26	- title change	다운로드
1.1	2018/11/26	- Comparison with conventional and fractional variant	다운로드
1.0.2	2018/8/15	- Simplification of code syntax	다운로드
1.0.1	2018/8/6	- Example added	다운로드
1.0.0	2018/8/6		다운로드

MLA	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.
APA	Khan, S., Naseem, I., Togneri, R., & Bennamoun, M. (2016). A Novel Adaptive Kernel for the RBF Neural Networks. Circuits, Systems, and Signal Processing, 36(4), 1639–1653. Springer Nature. Retrieved from https://doi.org/10.1007%2Fs00034-016-0375-7
BibTeX	@article{Khan_2016, doi = {10.1007/s00034-016-0375-7}, url = {https://doi.org/10.1007%2Fs00034-016-0375-7}, year = 2016, month = {jul}, publisher = {Springer Nature}, volume = {36}, number = {4}, pages = {1639--1653}, author = {Shujaat Khan and Imran Naseem and Roberto Togneri and Mohammed Bennamoun}, title = {A Novel Adaptive Kernel for the {RBF} Neural Networks}, journal = {Circuits, Systems, and Signal Processing} }

MLA	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.
APA	Khan, S., Naseem, I., Malik, M. A., Togneri, R., & Bennamoun, M. (2018). A Fractional Gradient Descent-Based RBF Neural Network. Circuits, Systems, and Signal Processing, 37(12), 5311–5332. Springer Nature America, Inc. Retrieved from https://doi.org/10.1007%2Fs00034-018-0835-3
BibTeX	@article{Khan_2018, doi = {10.1007/s00034-018-0835-3}, url = {https://doi.org/10.1007%2Fs00034-018-0835-3}, year = 2018, month = {may}, publisher = {Springer Nature America, Inc}, volume = {37}, number = {12}, pages = {5311--5332}, author = {Shujaat Khan and Imran Naseem and Muhammad Ammar Malik and Roberto Togneri and Mohammed Bennamoun}, title = {A Fractional Gradient Descent-Based {RBF} Neural Network}, journal = {Circuits, Systems, and Signal Processing} }

Nonlinear System Identification using Spatio-Temporal RBF-NN

인용 양식

필수 제품:

MATLAB 릴리스 호환 정보

플랫폼 호환성

카테고리

태그 태그 추가

도움

라이브 편집기 활용

Nonlinear_System_Identification