Soft computing techniques in modeling the influence of pH on dopamine biosensor

V. Rangelova, D. Tsankova
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引用次数: 2

Abstract

Recently the soft computing techniques have become an important alternative tool to conventional methods in modeling complex non-linear relationships. Since the electrochemical biosensors use an enzyme reaction for measuring different types of substrates, the pH and temperature influence strongly on the output signal of biosensors. The paper treats soft computing modeling the input/output non-linear dependence of a dopamine biosensor, which uses an active membrane from banana tissue. The model represents the biosensorpsilas output current versus the substrate concentration and pH. The temperature is not taken into account, it is set to be constant during the experiments. The problem to solve here is to find a way of increasing the accuracy (and the fastness) of the modeling process, under condition of insufficient experimental data. The following soft computing techniques are compared in MATLAB environment: (1) neural network with back-propagation learning algorithm, (2) CMAC neural network, (3) fuzzy logic, and (4) ANFIS. The relative errors over a few new experimental samples are calculated for validation of the proposed models.
模拟pH值对多巴胺生物传感器影响的软计算技术
近年来,软计算技术已成为复杂非线性关系建模中传统方法的重要替代工具。由于电化学生物传感器使用酶反应来测量不同类型的底物,pH和温度对生物传感器的输出信号有很大影响。本文对多巴胺生物传感器的输入/输出非线性依赖进行软计算建模,该传感器使用来自香蕉组织的活性膜。该模型表示生物传感器输出电流与底物浓度和ph的关系。温度不考虑在内,在实验过程中设置为恒定。这里要解决的问题是在实验数据不足的情况下,如何提高建模过程的准确性(和快速度)。在MATLAB环境下比较了以下软计算技术:(1)带反向传播学习算法的神经网络,(2)CMAC神经网络,(3)模糊逻辑,(4)ANFIS。计算了几个新实验样本上的相对误差,以验证所提出模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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