A self-organizing recurrent fuzzy CMAC model for dynamic system identification

Lin, Cheng‐Jian; Lee, Chi-Yung

doi:10.1002/int.20272

Cited by 8 publications

(9 citation statements)

References 17 publications

(14 reference statements)

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“…This configuration has the capability to use the past values of the global output and the local dynamic. Compared with other fuzzy CMAC networks [4,10,15,19], the recurrent fuzzy CMAC networks proposed in this paper have some significant differences and advantages. In [4] and [10], the fuzzy CMACs have feedforward structure, [4] used Tagaki-Sugeno fuzzy inference, while [10] used Mandani fuzzy inference.…”

Section: Introductionmentioning

confidence: 84%

“…Remark 1 Compared with other fuzzy CMAC networks [4,10,15,19], the recurrent fuzzy CMAC networks proposed in this paper have some significant differences and advantages.…”

Section: Fig 4 Recurrent Fuzzy Cmac With Global and Local Feedbacksmentioning

confidence: 99%

“…Time-varying learning rates are obtained by input-to-state stability (ISS) approach to update the parameters of the membership functions, these learning laws can assure stability in the training process. This is the second contribution of the paper, while [15] used gradient-descent algorithm without proof, [19] proved the stability of the feedforward fuzzy CMAC block. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 97%

“…They are suitable for static function approximation, but cannot be applied on dynamic system identification directly. In [15] and [19], recurrent structures are implemented on fuzzy CMAC as this paper. [15] used selforganizing technique to realize structure learning, it is similar as the local feedback fuzzy CMAC of this paper.…”

Section: Introductionmentioning

confidence: 99%

“…In [15] and [19], recurrent structures are implemented on fuzzy CMAC as this paper. [15] used selforganizing technique to realize structure learning, it is similar as the local feedback fuzzy CMAC of this paper. [19] used hierarchical structure, the last block can be regarded as global feedback fuzzy CMAC of this paper, the other blocks have feedforward structure as [4] and [10].…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC

Rodríguez

Moreno-Armendáriz³

2008

Neural Process Lett

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Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven.

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Section: Introductionmentioning

confidence: 84%

“…Remark 1 Compared with other fuzzy CMAC networks [4,10,15,19], the recurrent fuzzy CMAC networks proposed in this paper have some significant differences and advantages.…”

Section: Fig 4 Recurrent Fuzzy Cmac With Global and Local Feedbacksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC

Rodríguez

Moreno-Armendáriz³

2008

Neural Process Lett

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Development of a novel robust identification scheme for nonlinear dynamic systems

George

Panda

2014

Adaptive Control & Signal

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This paper presents a set of single layer low complexity nonlinear adaptive models for efficient identification of dynamic systems in the presence of outliers in the training signal. The weights of the new models have been updated using a new robust learning algorithm. The proposed robust algorithm is based on adaptive minimization of Wilcoxon norm of errors. The computational complexity associated with the new models has further been reduced by processing the input in block form and using a newly derived robust block learning algorithm. Through exhaustive simulation study of many benchmark identification examples, it has been shown that in all cases, the new models provide enhanced and robust identification performance compared with that provided by the corresponding conventional squared error-based approaches. Copyright by temporary or permanent breakdown of sensors, analog-to-digital conversion errors, failure of transducers, and so on.In the recent past, many robust learning algorithms, which are efficient in learning in the presence of outliers, have been proposed in the literature. A robust recursive least square method has been developed for identification of bilinear systems [18]. Another robust learning algorithm, which employs a soft filtering approach to remove the most prominent outliers before parameter estimation, has been derived in [19]. The technique has been applied to develop a robust recurrent neural network. Sanchez [20] has proposed a robust learning method for RBF networks, in which an RBF architecture is generated in the first phase using conventional learning scheme, and subsequently, a robust learning method is introduced to make the network robust against outliers. The mechanism by which outliers affect neural network models has been studied in [21] using the analysis of its influence function. The author also suggested the need for considering the use of error distribution functions with extensive tails, like the Cauchy or Lorentzian distribution. A mean log squared error cost function has also been proposed, which is more robust compared with conventional least mean square approach. Other prominent robust learning techniques include random sample consensus method [22] and M -estimator sampling consensus method [23].By combining conventional robust techniques with optimization of learning rate, a robust learning algorithm for optimizing fuzzy neural networks has been suggested in [24]. A robust fuzzy regression agglomeration clustering algorithm has been presented in [25] to develop Takagi-Sugeno-Kang models, which are robust against outliers. They have tested the performance through simulation studies. A hybrid robust Takagi-Sugeno-Kang modeling approach has been recently proposed in [26] by using a robust fuzzy C-regression model clustering algorithm in the coarse-tuning stage and an annealing robust back propagation learning algorithm in the fine-tuning phase. A robust support vector regression network has been proposed by employing traditional robust learning approaches to improve ...

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Adaptive fuzzy CMAC-based nonlinear control with dynamic memory architecture

Cheng

2011

Journal of the Franklin Institute

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A self-organizing recurrent fuzzy CMAC model for dynamic system identification

Cited by 8 publications

References 17 publications

Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC

Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC

Development of a novel robust identification scheme for nonlinear dynamic systems

Adaptive fuzzy CMAC-based nonlinear control with dynamic memory architecture

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