Abstract

In this study, estimation capacities and optimization of a dye concentration sensing model by an adapted neuro-fuzzy inference system (ANFIS) as well as central composite design coupled with response surface methodology using a plastic optical fiber (POF) based sensor were investigated. Various diameters $d$ of POF were used for sensing different concentrations of Remazol Black B (RBB), which acts as a sensing medium of the process. The efficiency of sensing was studied as a function of three independent variables: diameter of POF, concentration of RBB dye, and initial temperature of the solution. First, the independent parameters were fed as inputs to an ANFIS, and the output of the system was the output intensity of dye ratio to output the intensity of distilled water. ANFIS showed that this established model is reliable for a dye concentration sensing process and is mainly influenced by its diameter.

© 2022 Optica Publishing Group

Adaptive neuro-fuzzy inference system for generation of diffuser dot patterns in light guides

Heng Zhao, Suping Fang, and Bo Shang
Appl. Opt. 49(14) 2694-2702 (2010)

Adaptive neuro fuzzy selection of important factors for prediction of plasmons in silver nanorods

Dalibor Petković, Mohamed Amine Khadimallah, Yan Cao, Nebojsa Denic, Vuk Vujovic, Dragan Zlatkovic, and Jelena Stojanovic
Appl. Opt. 61(10) 2864-2868 (2022)

Examination of tapered plastic multimode fiber-based sensor performance with silver coating for different concentrations of calcium hypochlorite by soft computing methodologies—a comparative study

Rozalina Zakaria, Ong Yong Sheng, Kam Wern, Shahaboddin Shamshirband, Ainuddin Wahid Abdul Wahab, Dalibor Petković, and Hadi Saboohi
J. Opt. Soc. Am. A 31(5) 1023-1030 (2014)

Refractive index sensor based on a multi-notched plastic optical fiber

Chuanxin Teng, Ning Jing, Fangda Yu, Yue Ding, and Jie Zheng
Appl. Opt. 56(7) 1833-1838 (2017)

Wet etching technique for fabrication of a high-quality plastic optical fiber sensor

Mingfu Zhao, Lang Dai, Nianbing Zhong, Zhengkun Wang, Ming Chen, Bingxin Li, Binbin Luo, Bin Tang, Shenghui Shi, Tao Song, and Xue Zou
Appl. Opt. 56(31) 8845-8850 (2017)