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首页> 外文期刊>IEEE Transactions on Neural Networks >A 'mutual update' training algorithm for fuzzy adaptive logic control/decision network (FALCON)
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A 'mutual update' training algorithm for fuzzy adaptive logic control/decision network (FALCON)

机译：模糊自适应逻辑控制/决策网络（FALCON）的“相互更新”训练算法

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摘要

The conventional two-stage training algorithm of the fuzzyeural architecture, called FALCON, may not provide accurate results for certain type of problems, due to the implicit assumption of independence that this training makes about parameters of the underlying fuzzy inference system. In this paper, a training scheme is proposed for this fuzzyeural architecture, which is based on line search methods that have long been used in iterative optimization problems. This scheme involves synchronous update of the parameters of the architecture corresponding to input and output space partitions and rules defining the underlying mapping; the magnitude and direction of the update at each iteration is determined using the Armijo rule. In our motor fault detection study case, the mutual update algorithm arrived at the steady-state error of the conventional FALCON training algorithm is twice as fast and produced a lower steady-state error by an order of magnitude.
机译：模糊/神经体系结构的传统两阶段训练算法FALCON，由于隐式假设该训练对基础模糊推理系统的参数具有独立性，因此可能无法为某些类型的问题提供准确的结果。在本文中，针对这种模糊/神经架构，提出了一种训练方案，该方案基于长期用于迭代优化问题的线搜索方法。该方案涉及同步更新与输入和输出空间分区以及定义基础映射的规则相对应的体系结构参数；使用Armijo规则确定每次迭代时更新的大小和方向。在我们的电机故障检测研究案例中，相互更新算法得出的常规FALCON训练算法的稳态误差快两倍，并且产生的稳态误差低一个数量级。

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