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Adaptive neural-based fuzzy modeling for biological systems

机译：基于自适应神经网络的生物系统模糊建模

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摘要

The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say " driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems.
机译：从其时程响应数据集中识别动态生物网络的反问题是系统生物学的基石。 Hill和Michaelis-Menten模型是一种前向方法，可提供局部动力学信息。然而，重复的修改和大量的实验数据对于参数识别是必需的。由高度非线性的微分方程组成的S系统模型可直接识别交互式网络。然而，骨骼网络结构的鉴定是具有挑战性的。而且，生物系统总是容易受到不确定性和噪声的影响。是否有合适的候选人可能处理受噪声污染的数据集？模糊集理论是为处理现实世界中的不确定性，不精确性和复杂性而开发的;例如，我们说“行驶速度很高”，其中速度是一个模糊变量，而“ high”是一个模糊集，它使用隶属函数表示属于该集合的元素的程度（斜体字表示模糊变量或模糊集）集）。神经网络具有良好的鲁棒性和学习能力。在这项研究中，我们将这两者混合在一起成为神经模糊建模技术。将生物系统公式化为由基于规则的线性子系统组成的多输入多输出（MIMO）高木-Sugeno（T-S）模糊系统。使用两种平滑隶属函数（MF），即高斯和钟形MF。所提出的方法的性能通过三种生物系统进行了测试。

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