Fuzzy modeling the influence of temperature on tissue biosensor for measurement of dopamine

Soft Computing Techniques in Modelling the Influence of pH and Temperature on Dopamine Biosensor

“…In conformity with the results, reported in (Rangelova & Tsankova, 2007b;Rangelova & Tsankova, 2008) and confirmed in the next Section, the triangular shape of membership functions (Fig.7) and T-norm (using the multiplication operator) of the membership values on the premise part are chosen. The apexes of the triangles are exactly the measured values of substrate, pH and temperature.…”

“…Such neural networks have some drawbacks: (1) the proper number of hidden layers and the number of neurons in them are not known in advance, (2) the learning is a time consuming process, which often gets stuck in local minima, (3) the neural network could not generalize, if the training samples are insufficient. The CMAC-neural-network-based model of the biosensor input/output has overcome some of the drawbacks, however it needs sufficient number of (Rangelova & Tsankova 2007a). The use of interpolated data is justified under the lack of data, because of difficulties associated with their experimental acquisition, but it reduces the main advantage of a neural model -the high accuracy.…”

“…The use of interpolated data is justified under the lack of data, because of difficulties associated with their experimental acquisition, but it reduces the main advantage of a neural model -the high accuracy. Two fuzzy logic based models of a dopamine biosensor that take into account the influence of temperature (first model) and pH (the other) have been recently proposed in (Rangelova & Tsankova, 2007b) and (Rangelova & Tsankova, 2008), respectively. This technique has been found to perform well under imprecise and insufficient experimental data.…”

A Thorough Insight to Techniques for Performance Evaluation in Biological Sensors

“…The paper of Ranglova et al [31] presents a fuzzy based approach of biosensor input/output necessity Simulations performed on various software's confirms the high accuracy of fuzzy logic method. Relative errors which have been come out with the calculation of experimental data, used for the legitimacy of the proposed technique.…”

“…However the outcomes are not found reliable as adoption of such algorithm are found with certain flaws e.g. i) inability or no assured to find global optimum [31], [32], ii) inability to ensure uniform optimization during response in performance validation of sensors [33], and iii) processing time of algorithm increases with increase in network size [38]- [39].…”