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Introducing validity into self-organizing fuzzy neural network applied to impedance force control

机译：将有效性引入用于阻抗力控制的自组织模糊神经网络

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In this paper, a novel self-organizing fuzzy neural network is proposed that constructed by an input-output mapping and monitored by a hierarchy of validity degrees. We define new operators called validification and devalidification to propagate validity into the six layers of proposed architecture. Self-organizing in structure learning is accomplished through a new measure that depends on error, the number of rules, and validity degrees. Additionally, a parameter learning condition is derived through studying the stability of proposed approach. To evaluate the proposed approach, an impedance controller is designed facing three challenges: different kinds of uncertainty, partial truth, and real-time realization. In the proposed controller, considering the challenge of partial truth, we assume that the manipulator's inertia is known according to first-principle knowledge while other parts are uncertain. Simulation results show the effectiveness of the proposed approach in the presence of disturbance. The proposed approach emerges as a promising approach by involving self-organizing property and possibility (fuzzy) and validity aspects of information. (c) 2017 Elsevier B.V. All rights reserved.
机译：本文提出了一种新的自组织模糊神经网络，该神经网络通过输入-输出映射来构建，并通过有效性程度的层次结构进行监控。我们定义了新的运算符，称为验证和取消验证，以将有效性传播到提议的体系结构的六层中。通过一种取决于错误，规则数量和有效性程度的新措施，可以完成结构学习中的自组织。另外，通过研究所提出方法的稳定性，得出了参数学习条件。为了评估所提出的方法，设计了一种阻抗控制器，它面临三个挑战：不同类型的不确定性，部分真实性和实时实现。在提出的控制器中，考虑到部分真实性的挑战，我们假设根据第一原理知识已知机械手的惯性，而其他部分不确定。仿真结果表明了该方法在存在干扰的情况下的有效性。通过涉及信息的自组织属性和可能性（模糊性）和有效性方面，提出的方法成为一种有前途的方法。 （c）2017 Elsevier B.V.保留所有权利。

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