Abstract
This study proposes a Self-evolving Takagi-Sugeno-Kang-type Fuzzy Cerebellar Model Articulation Controller (STFCMAC) for solving identification and prediction problems. The proposed STFCMAC model uses the hypercube firing strength for generating external loops and internal feedback. A differentiable Gaussian function is used in the fuzzy hypercube cell of the proposed model, and a linear combination function of the model inputs is used as the output of the proposed model. The learning process of the STFCMAC is initiated using an empty hypercube base. Fuzzy hypercube cells are generated through structure learning, and the related parameters are adjusted by a gradient descent algorithm. The proposed STFCMAC network has some advantages that are summarized as follows: (1) the model automatically selects the parameters of the memory structure, (2) it requires few fuzzy hypercube cells, and (3) it performs identification and prediction adaptively and effectively.
Highlights
During the past decade, neural networks (NNs) have been widely used in dynamic system applications, such as control, identification, prediction, and signal processing [1,2,3,4]
We extend our previous study [18] by developing a Self-evolving Takagi-SugenoKang-type Fuzzy Cerebellar Model Articulation Controller (STFCMAC) model
The three major contributions of the proposed Sugeno-Kang-type Fuzzy Cerebellar Model Articulation Controller (STFCMAC) are summarized as follows: (1) the model automatically selects the parameters of the memory structure, (2) it requires few fuzzy hypercube cells, and (3) it performs identification and prediction adaptively and effectively
Summary
Neural networks (NNs) have been widely used in dynamic system applications, such as control, identification, prediction, and signal processing [1,2,3,4]. Compared with the CMAC, the FCMAC uses fuzzy membership functions to model the problem. To improve the function approximation ability, Lee et al [18] proposed a parametric FCMAC (PFCMAC) that is a hybrid of a Takagi–Sugeno–Kang (TSK)-type fuzzy inference system [19] and a CMAC network. The PFCMAC can approximate continuous functions and minimize the number of hypercube cells These models use a feedforward structure and cause instability problems due to the local nature of hypercube cells. The three major contributions of the proposed STFCMAC are summarized as follows: (1) the model automatically selects the parameters of the memory structure, (2) it requires few fuzzy hypercube cells, and (3) it performs identification and prediction adaptively and effectively. In Layer 5, a centroid of area approach is adopted to obtain the model output
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