Medical diagnosis applications using a novel interactively recurrent self-evolving fuzzy CMAC model

Wang, Jyun Guo; Tai, Shen-Chuan; Cheng, Lin

doi:10.1109/ijcnn.2014.6889511

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Figure 1 shows the proposed six-layer RFCMAC structure. 17,18 The Takagi-Sugeno-Kang-type (TSK-type) RFCMAC model realizes a similar fuzzy IF-THEN rule (hypercube cell) as follows.…”

Section: The Proposed Rfcmac Modelmentioning

confidence: 99%

Mobile robot navigation control using recurrent fuzzy cerebellar model articulation controller based on improved dynamic artificial bee colony

Lin

Huang

Kuo

et al. 2016

Advances in Mechanical Engineering

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In this study, an efficient navigation control method of mobile robot is proposed. The proposed navigation control method consists of behavior manager, toward goal behavior, and wall-following behavior. According to the relative position between the mobile robot and the environment, the behavior manager switches to determine toward goal behavior or wall-following behavior of mobile robot. A novel recurrent fuzzy cerebellar model articulation controller based on an improved dynamic artificial bee colony is proposed for performing wall-following control of mobile robot. The proposed improved dynamic artificial bee colony algorithm uses the sharing mechanism and the dynamic identity update to improve the performance of optimization. A reinforcement learning method is adopted to train the wall-following control of mobile robot. Experimental results show that the proposed method obtains a better navigation control than other methods in unknown environment.

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“…Figure 1 shows the proposed six-layer RFCMAC structure. 17,18 The Takagi-Sugeno-Kang-type (TSK-type) RFCMAC model realizes a similar fuzzy IF-THEN rule (hypercube cell) as follows.…”

Section: The Proposed Rfcmac Modelmentioning

confidence: 99%

Mobile robot navigation control using recurrent fuzzy cerebellar model articulation controller based on improved dynamic artificial bee colony

Lin

Huang

Kuo

et al. 2016

Advances in Mechanical Engineering

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“…Compared with other neural networks, CMAC is advantageous insofar that it has fast learning properties, simple computations, and good generalization capabilities [13]. In the past decade, CMAC has been applied to various fields, such as control systems [14][15][16][17], classification systems [18][19][20], signal processing [21][22][23], and image processing [24,25]. Due to the work of Zadeh [26], fuzzy modeling and fuzzy control have attracted many researchers since said methods can be used to convert problems into simple human terms.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Synchronization Using a Self-Evolving Recurrent Interval Type-2 Petri Cerebellar Model Articulation Controller

Lin

Quynh

et al. 2020

Mathematics

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In this manuscript, the synchronization of four-dimensional (4D) chaotic systems with uncertain parameters using a self-evolving recurrent interval type-2 Petri cerebellar model articulation controller is studied. The design of the synchronization control system is comprised of a recurrent interval type-2 Petri cerebellar model articulation controller and a fuzzy compensation controller. The proposed network structure can automatically generate new rules or delete unnecessary rules based on the self-evolving algorithm. Furthermore, the gradient-descent method is applied to adjust the proposed network parameters. Through Lyapunov stability analysis, bounded system stability is guaranteed. Finally, the effectiveness of the proposed controller is illustrated using numerical simulations of 4D chaotic systems.

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“…Recently it has been widely used especially in the adaptive control problems, e.g., [9]- [13]. Another application and investigation of the CMAC NN is given in [14] for medical diagnosis and in [15], [16] for filling gaps in tables and lines. The large-scale applications of the CMAC NN are related to its main characteristics: working in the discrete space of the component of the input vector, nonlinear transformation of the input vector with the algorithm of memory active cells numbers calculation and fast on-line learning compared with the MNN.…”

Section: Introductionmentioning

confidence: 99%

Basis Functions of the Multidimensional CMAC Neural Network

Aved’yan¹

2014

2014 International Conference on Engineering and Telecommunication

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The short introduction to the cerebellar model articulation controller (the CMAC neural network) and its main properties are presented. It is shown that the basis functions of the CMAC neural network are given implicitly, the number of these function is finite, and they have the incorrect pyramid forms. The expressions for the number of these function is given. Keywords-CMAC neural network; learning algorithm ; basis functionsI.

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Medical diagnosis applications using a novel interactively recurrent self-evolving fuzzy CMAC model

Cited by 5 publications

References 17 publications

Mobile robot navigation control using recurrent fuzzy cerebellar model articulation controller based on improved dynamic artificial bee colony

Mobile robot navigation control using recurrent fuzzy cerebellar model articulation controller based on improved dynamic artificial bee colony

Chaotic Synchronization Using a Self-Evolving Recurrent Interval Type-2 Petri Cerebellar Model Articulation Controller

Basis Functions of the Multidimensional CMAC Neural Network

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