Fuzzy Logic, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference Methodology for Soft Computation and Modeling of Ion Sensing Data of a Terpyridyl-Imidazole Based Bifunctional Receptor

Abstract

Anion and cation sensing aspects of a terpyridyl-imidazole based receptor have been utilized in this work for the fabrication of multiply configurable Boolean and fuzzy logic systems. The terpyridine moiety of the receptor is used for cation sensing through coordination, whereas the imidazole motif is utilized for anion sensing via hydrogen bonding interaction and/or anion-induced deprotonation, and the recognition event was monitored through absorption and emission spectroscopy. The receptor functions as a selective sensor for F− and Fe2+ among the studied anions and cations, respectively. Interestingly, the complexation of the receptor by Fe2+ and its decomplexation by F− and deprotonation of the receptor by F− and restoration to its initial form by acid are reversible and can be recycled. The receptor can mimic various logic operations such as combinatorial logic gate and keypad lock using its spectral responses through the sequential use of ionic inputs. Conducting very detailed sensing studies by varying the concentration of the analytes within a wide domain is often very time-consuming, laborious, and expensive. To decrease the time and expenses of the investigations, soft computing approaches such as artificial neural networks (ANNs), fuzzy logic, or adaptive neuro-fuzzy inference system (ANFIS) can be recommended to predict the experimental spectral data. Soft computing approaches to artificial intelligence (AI) include neural networks, fuzzy systems, evolutionary computation, and other tools based on statistical and mathematical optimizations. This study compares fuzzy, ANN, and ANFIS outputs to model the protonation-deprotonation and complexation-decomplexation behaviors of the receptor. Triangular membership functions (trimf) are used to model the ANFIS methodology. A good correlation is observed between experimental and model output data. The testing root mean square error (RMSE) for the ANFIS model is 0.0023 for protonation-deprotonation and 0.0036 for complexation-decomplexation data.

Polyacrylonitrile Passivation for Enhancing the Optoelectronic Switching Performance of Halide Perovskite Memristor for Image Boolean Logic Applications

“…In fact, perovskite optoelectronic memristors can also achieve continuous changing of resistance states by modulating the stimuli (voltage pulses) [37]. In the future, we can also regulate light and voltage and monitor the input-output relationship in a continuous way, implementing fuzzy logic operations based on multi-valued logic [38,39].…”

Improved Facial Identification Using Adaptive Neuro-Fuzzy Logic Inference System

“…FR is a pattern recognition method that is connected to the notion of artificial intelligence. It is a difficult challenging topic of research since real-time face pictures are created by the merging of numerous components under different settings such as changes in lighting, interference of background, facial position fluctuation, and so on (2)(3)(4)(5) . The two most crucial processes in our automated FR system are feature extraction and classification (6)(7)(8) .…”

Human brain-inspired chemical artificial intelligence tools for the analysis and prediction of the anion-sensing characteristics of an imidazole-based luminescent Os(ii)-bipyridine complex

“…Obtaining a quantitative insight of a particular observation either through theoretical calculations or by experimentation is always associated with the consumption of time. We employed, in this work, various deep learning and machine learning tools, such as fuzzy logic, [11][12][13][14][15][16] artificial neural network (assisted by three different training algorithms), [17][18][19][20][21][22] adaptive neuro-fuzzy inference system, [23][24][25][26][27] as well as decision tree regression analysis, [28][29][30][31][32][33][34] on our existing anion-sensing dataset of an Os(II)polyheterocyclic complex for proper understanding as well as to fully predict its anion-sensing characteristic within a very short period of time (Chart 1). 35 We utilized herein an imidazolyl bis-benzimidazole-based Os(II) complex having three azole NH motifs in its periphery (Chart 1).…”

“…The structure of adaptive neuro-fuzzy inference system has the same framework as the fuzzy logic barring the neural network grid. [23][24][25][26][27] Finally, the decision tree-based technique is also used to fully understand the sensing aspects of the complex as it emerged as a nonparametric method possessing the unique attributes of uniting interpretability and efficacy, and when applied in ensembles of trees, good accuracy. [28][29][30][31][32][33][34] A schematic sketch of a decision tree will be provided in the subsequent section.…”