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FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC

机译:FCMAC-Yager:基于Yager推理方案的新型模糊CMAC

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The cerebellum is a brain region important for a number of motor and cognitive functions. It is able to generate error correction signals to drive learning and for the acquisition of memory skills. The cerebellar model articulation controller (CMAC) is a neural network inspired by the neurophysiologic theory of the cerebellum and is recognized for its localized generalization and rapid algorithmic computation capabilities. The main deficiencies in the basic CMAC structure are: 1) it is difficult to interpret the internal operations of the CMAC network and 2) the resolution (quantization) problem arising from the partitioning of the input training space. These limitations lead to the synthesis of a fuzzy quantization technique and the mapping of a fuzzy inference scheme onto the CMAC structure. The discrete incremental clustering (DIC) technique is employed to alleviate the quantization problem in the CMAC structure, resulting in the fuzzy CMAC (FCMAC) network. The Yager inference scheme (Yager), which possesses firm fuzzy logic foundation and maps closely to the logical implication operations in the classical (binary) logic framework, is subsequently mapped onto the FCMAC structure. This results in a novel fuzzy neural architecture known as the fuzzy cerebellar model articulation controller-Yager (FCMAC-Yager) system. The proposed FCMAC-Yager network exhibits learning and memory capabilities of the cerebellum through the CMAC structure while emulating the human way of reasoning through the Yager. The new FCMAC-Yager network employs a two-phase training algorithm consisting of structural learning based on the DIC technique and parameter learning using hebbian learning (associative long-term potentiation). The proposed FCMAC-Yager architecture is evaluated using an extensive suite of real-life applications such as highway traffic-trend modeling and prediction and performing as an early warning system for bank failure classification and medical diagnosis of breast canc-er. The experimental results are encouraging
机译:小脑是对许多运动和认知功能很重要的大脑区域。它能够产生错误校正信号,以驱动学习并获得记忆技能。小脑模型关节控制器(CMAC)是受小脑神经生理学理论启发的神经网络,因其局部概括和快速算法计算能力而得到认可。基本CMAC结构的主要缺陷是:1)难以解释CMAC网络的内部操作,以及2)输入训练空间的划分引起的分辨率(量化)问题。这些局限性导致了模糊量化技术的综合以及模糊推理方案到CMAC结构上的映射。采用离散增量聚类(DIC)技术缓解了CMAC结构中的量化问题,从而形成了模糊CMAC(FCMAC)网络。随后,将具有牢固的模糊逻辑基础并与经典(二进制)逻辑框架中的逻辑蕴涵操作紧密映射的Yager推理方案(Yager)映射到FCMAC结构。这导致了一种新颖的模糊神经体系结构,称为模糊小脑模型关节控制器-Yager(FCMAC-Yager)系统。提出的FCMAC-Yager网络通过CMAC结构展示了小脑的学习和记忆能力,同时通过Yager模拟了人类的推理方式。新的FCMAC-Yager网络采用了两阶段的训练算法,该算法包括基于DIC技术的结构学习和使用hebbian学习(关联长期增强)的参数学习。拟议的FCMAC-Yager体系结构使用了一系列广泛的实际应用程序进行评估,例如高速公路交通趋势建模和预测,并可以作为早期预警系统,用于银行故障分类和乳腺癌的医学诊断。实验结果令人鼓舞

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