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Distributed Adaptive Neuro Intuitionistic Fuzzy Architecture for prediction of the dose in gamma irradiated milk products

机译：分布式自适应神经直觉模糊体系结构，用于预测γ辐照乳制品中的剂量

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In this paper, a Distributed Adaptive Neuro Intuitionistic Fuzzy Architecture (DANIFA) with a second order Takagi-Sugeno inference is presented. The architecture represents a layered set of simple fuzzy inferences connected in a distributed way, thus minimizing the number of the interconnected fuzzy rules and their associated parameters. The flexibility of the designed structure to handle uncertain data variations is complemented, by embedding an Intuitionistic fuzzification approach. A simple two-step gradient descent algorithm with a fixed learning rate is used as a learning algorithm of the proposed architecture. To test the prediction abilities of the designed model a biological case for estimation of the low gamma irradiation dose to destruct the protein fractions in milk products with potential uncertain data variations is studied.
机译：本文提出了一种具有二阶Takagi-Sugeno推断的分布式自适应神经直觉模糊体系结构（DANIFA）。该体系结构表示以分布式方式连接的一组简单模糊推理的分层集合，从而使互连的模糊规则及其相关参数的数量最少。通过嵌入直觉的模糊化方法，可补充设计结构处理不确定数据变化的灵活性。具有固定学习速率的简单两步梯度下降算法被用作所提出的体系结构的学习算法。为了检验所设计模型的预测能力，研究了一种生物学案例，用于估算低伽玛射线辐照剂量，以破坏奶制品中蛋白质组分的潜在不确定数据变化。

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