A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks

Wu, Shixin; Er, Meng Joo; Ni, Maolin; Leithead, W.E.

doi:10.1109/acc.2000.878622

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…Online identification commonly uses self-organizing methods to adjust the number of neurons through a series of structural identification indexes. For example, Wu et al [11,12] proposed that the dynamic fuzzy neural network (D-FNN) and its enhanced version of the generalized dynamic fuzzy neural network (GD-FNN) can be used for online structure identification and parameter optimization, but the generalization performance of the system is poor. Han et al [13] used the relative importance index of each rule to achieve the self-organization of FNN and [14] used the information theoretic approach to design fuzzy rules by dividing fuzzy rules with high peak intensity into new rules and branching fuzzy rules with small relative mutual information (SOFNN-ACA).…”

Section: Introductionmentioning

confidence: 99%

Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm

Zhang

Wang

2022

Applied Sciences

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In this paper, an improved self-organizing fuzzy neural network (SOFNN-CA) based on a clustering algorithm is proposed for nonlinear systems modeling in industrial processes. In order to reduce training time and increase training speed, we combine offline learning and online identification. The unsupervised clustering algorithm is used to generate the initial centers of the network in the offline learning phase, and, in the self-organizing phase of the system, the Mahalanobis distance (MD) index and error criterion are adopted to add neurons to learn new features. A new density potential index (DPI) combined with neuron local field potential (LFP) is designed to adjust the neuron width, which further improves the network generalization. The similarity index calculated by the Gaussian error function is used to merge neurons to reduce redundancy. Meanwhile, the convergence of SOFNN-CA in the case of structural self-organization is demonstrated. Simulations and experiments results show that the proposed SOFNN-CA has a more desirable modeling accuracy and convergence speed compared with SOFNN-ALA and SOFNN-AGA.

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Section: Introductionmentioning

confidence: 99%

Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm

Zhang

Wang

2022

Applied Sciences

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“…Fuzzy neural networks can be effective for incomplete and inaccurate process information modeling and analysis [ 11 ]. Several algorithms, combining neural networks and fuzzy processing, have been proposed for signal analysis.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Radial Basis Adaptive Inference Network and Its Application to Time‐Varying Signal Classification

Huang

Liu

et al. 2021

Computational Intelligence and Neuroscience

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A fuzzy radial basis adaptive inference network (FRBAIN) is proposed for multichannel time-varying signal fusion analysis and feature knowledge embedding. The model which combines the prior signal feature embedding mechanism of the radial basis kernel function with the rule-based logic inference ability of fuzzy system is composed of a multichannel time-varying signal input layer, a radial basis fuzzification layer, a rule layer, a regularization layer, and a T-S fuzzy classifier layer. The dynamic fuzzy clustering algorithm was used to divide the sample set pattern class into several subclasses with similar features. The fuzzy radial basis neurons (FRBNs) were defined and used as parameterized membership functions, and typical feature samples of each pattern subclass were used as kernel centers of the FRBN to realize the embedding of the diverse prior feature knowledge and the fuzzification of the input signals. According to the signal categories of FRBN kernel centers, nodes in the rule layer were selectively connected with nodes in the FRBN layer. A fuzzy multiplication operation was used to achieve synthesis of pattern class membership information and establishment of fuzzy inference rules. The excitation intensity of each rule was used as the input of T-S fuzzy classifier to classify the input signals. The FRBAIN can adaptively establish fuzzy set membership functions, fuzzy inference, and classification rules based on the learning of sample set, realize structural and data constraints of the model, and improve the modeling properties of imbalanced datasets. In this paper, the properties of FRBAIN were analyzed and a comprehensive learning algorithm was established. Experimental validation was performed with classification diagnoses from four complex cardiovascular diseases based on 12-lead ECG signals. Results demonstrated that, in the case of small-scale imbalanced datasets, the proposed method significantly improved both classification accuracy and generalizability comparing with other methods in the experiment.

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Adaptive probability scheme for behaviour monitoring of the elderly using a specialised ambient device

Winkley

Jiang

2012

Int. J. Mach. Learn. & Cyber.

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A Hidden Markov Model (HMM) modified to work in combination with a Fuzzy System is utilised to determine the current behavioural state of the user from information obtained with specialised hardware. Due to the high dimensionality and not-linearly-separable nature of the Fuzzy System and the sensor data obtained with the hardware which informs the state decision, a new method is devised to update the HMM and replace the initial Fuzzy System such that subsequent state decisions are based on the most recent information. The resultant system first reduces the dimensionality of the original information by using a manifold representation in the high dimension which is unfolded in the lower dimension. The data is then linearly separable in the lower dimension where a simple linear classifier, such as the perceptron used here, is applied to determine the probability of the observations belonging to a state. Experiments using the new system verify its applicability in a real scenario.

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A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks

Cited by 8 publications

References 27 publications

Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm

Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm

A Fuzzy Radial Basis Adaptive Inference Network and Its Application to Time‐Varying Signal Classification

Adaptive probability scheme for behaviour monitoring of the elderly using a specialised ambient device

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