A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks

Castro, Juan R.; Castillo, Oscar; Melín, Patricia; Rodríguez-Díaz, Antonio

doi:10.1016/j.ins.2008.10.016

Cited by 270 publications

(91 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feuring [39] presented a new backpropagation algorithm for learning in the neural network, in which the new lower and upper limits of weights are computed. Castro et al [40] proposed a type-2 fuzzy neurons model, in which the rules used interval type-2 fuzzy neurons in the antecedents and an interval of type-1 fuzzy neuron in the consequents.…”

Section: Proposed Methodologymentioning

confidence: 99%

Comparison of T-Norms and S-Norms for Interval Type-2 Fuzzy Numbers in Weight Adjustment for Neural Networks

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract:A comparison of different T-norms and S-norms for interval type-2 fuzzy number weights is proposed in this work. The interval type-2 fuzzy number weights are used in a neural network with an interval backpropagation learning enhanced method for weight adjustment. Results of experiments and a comparative research between traditional neural networks and the neural network with interval type-2 fuzzy number weights with different T-norms and S-norms are presented to demonstrate the benefits of the proposed approach. In this research, the definitions of the lower and upper interval type-2 fuzzy numbers with random initial values are presented; this interval represents the footprint of uncertainty (FOU). The proposed work is based on recent works that have considered the adaptation of weights using type-2 fuzzy numbers. To confirm the efficiency of the proposed method, a case of data prediction is applied, in particular for the Mackey-Glass time series (for τ = 17). Noise of Gaussian type was applied to the testing data of the Mackey-Glass time series to demonstrate that the neural network using a interval type-2 fuzzy numbers method achieves a lower susceptibility to noise than other methods.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Comparison of T-Norms and S-Norms for Interval Type-2 Fuzzy Numbers in Weight Adjustment for Neural Networks

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…The settings to generate MG time series in this paper are selected from the literature [10,17,18]. Specifically, the fourth-order Runge-Kutta method is used and the initial settings are: time step = 0.1; x(0) = 1.2 and x(t) = 0 for t < 0; τ = 17. x(t) is thus derived for 0 t 1200.…”

Section: A Mackey-glass Time Series Prediction 1) Mackey-glass Time mentioning

confidence: 99%

Type-1 and interval type-2 ANFIS: A comparison

Chen

John

Twycross

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

Abstract-In a previous paper, we proposed an extended ANFIS architecture and showed that interval type-2 ANFIS produced larger errors than type-1 ANFIS on the well-known IRIS classification problem. In this paper, more experiments on both synthetic and real-world data are conducted to further investigate and compare the performance of interval type-2 ANFIS and type-1 ANFIS. For each dataset, interval type-2 ANFIS is optimised in three different ways, including a strategy suggested by Mendel such that interval type-2 ANFIS would be no worse than type-1 ANFIS. Our results show that in some circumstances the performance of interval type-2 ANFIS can be improved when it is initialised with blurred optimised type-1 ANFIS parameters. However, in general, interval type-2 ANFIS does not produce a clear performance improvement compared to type-1 ANFIS, especially on Mackey-Glass data with large noise. Thus, we conclude that the choice of interval type-2 ANFIS over type-1 ANFIS should be carefully considered, since type-2 ANFIS is more computationally complex, yet significantly better performance cannot be easily obtained.

show abstract

“…Variants of FCM [21][22][23][24] have been described with modified definitions for the norm and prototypes of the cluster centroids [26][27][28]. FCM clusters each data point to one or more clusters, and partitions a set of data x i ∈ R d , i = 1, 2, .…”

Section: Basic Procedures and Proposed Algorithm 21 Fuzzy C-means Clumentioning

confidence: 99%

A New Mechanism for Data Visualization with Tsk-Type Preprocessed Collaborative Fuzzy Rule Based System

Prasad

Liu

et al. 2016

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of TakagiSugeno-Kang type model is presented in this paper. The proposed method divides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within eachother. The proposed method is useful in dealing with big data issues since it divides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only half or less/more than the half of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show the proposed method performs better than existing methods on some benchmark problems.

show abstract

A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks

Cited by 270 publications

References 31 publications

Comparison of T-Norms and S-Norms for Interval Type-2 Fuzzy Numbers in Weight Adjustment for Neural Networks

Comparison of T-Norms and S-Norms for Interval Type-2 Fuzzy Numbers in Weight Adjustment for Neural Networks

Type-1 and interval type-2 ANFIS: A comparison

A New Mechanism for Data Visualization with Tsk-Type Preprocessed Collaborative Fuzzy Rule Based System

Contact Info

Product

Resources

About