Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction

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

doi:10.1007/s00500-013-1139-y

Cited by 103 publications

(23 citation statements)

References 32 publications

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“…It has also been shown that in the presence of perturbations, the generalized type-2 fuzzy controllers outperform their type-1 and interval type-2 counterparts [30], [31], and that interval type-2 NSFLSs outperform type-1 NSFLSs in different application domains [32], [33]. Incorporating other AI techniques with type-2 FLSs has also resulted in hybrid solutions for predicting the behaviour of non-linear complex systems (e.g., using neural networks in [34] and genetic algorithms in [35]). …”

Section: Other Related Workmentioning

confidence: 99%

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Pourabdollah

Wagner

Aladi

et al. 2016

IEEE Trans. Fuzzy Syst.

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(2016) Improved uncertainty capture for non-singleton fuzzy systems. IEEE Transactions on Fuzzy Systems, 24 (6). pp. 1513 -1524 . ISSN 1941 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/45444/1/Improved%20Uncertainty%20Capture%20for %20Non-Singleton%20Fuzzy%20Systems.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the University of Nottingham End User licence and may be reused according to the conditions of the licence. For more details see: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk 1 Improved Uncertainty Capture for Non-Singleton Fuzzy SystemsAmir Pourabdollah, Member, IEEE, Christian Wagner, Senior Member, IEEE, Jabran Hussain Aladi, Student Member, IEEE and Jonathan M. Garibaldi, Member, IEEE Abstract-In non-singleton fuzzy logic systems (NSFLSs), input uncertainties are modelled with input fuzzy sets in order to capture input uncertainty (e.g., sensor noise). The performance of NSFLSs in handling such uncertainties depends on both: the appropriate modelling in the input fuzzy sets of the uncertainties present in the system's inputs, and on how the input fuzzy sets (and their inherent model of uncertainty) interact with the antecedent and thus affect the inference within the remainder of the NSFLS. This paper proposes a novel development on the latter. Specifically, an alteration to the standard composition method of type-1 fuzzy relations is proposed, and applied to build a new type of NSFLS. The proposed approach is based on employing the centroid of the intersection of input and antecedent sets as origin of the firing degree, rather than the traditional maximum of their intersection, thus making the NSFLS more sensitive to changes in the input's uncertainty characteristics. The traditional and novel approach to NSFLSs are experimentally compared for two well-known problems of Mackey-Glass and Lorenz chaotic time series predictions, where the NSFLSs' inputs have been perturbed with different levels of Gaussian noise. Experiments are repeated for system training under noisy and noise-free conditions. Analyses of the results show that the new method outperforms the traditional approach. Moreover, it is shown that while formally more complex, in practice, the new method has no significant computational overhead compared to the standard approach.

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Section: Other Related Workmentioning

confidence: 99%

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Pourabdollah

Wagner

Aladi

et al. 2016

IEEE Trans. Fuzzy Syst.

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“…It proves that the predictive outputs followed the process very well. At the same time, the modeling error, with respect to the data of condition 1, is listed in Table 3 along with the results from other methods as reported in [23,[26][27][28][29]. Comparison between various methods shows that the ABFT-SMM can reach a better degree of accuracy.…”

Section: Comparison and Discussionmentioning

confidence: 93%

“…The benchmark problem of model identification, predicting the time series generated by the chaotic Mackey-Glass differential delay equation, can be expressed as follows [23][24][25][26][27][28][29]:…”

Section: Description Of the Equationmentioning

confidence: 99%

Agent Based Fuzzy T-S Multi-Model System and Its Applications

Zhao

2015

Applied Sciences

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Based on the basic concepts of agent and fuzzy T-S model, an agent based fuzzy T-S multi-model (ABFT-SMM) system is proposed in this paper. Different from the traditional method, the parameters and the membership value of the agent can be adjusted along with the process. In this system, each agent can be described as a dynamic equation, which can be seen as the local part of the multi-model, and it can execute the task alone or collaborate with other agents to accomplish a fixed goal. It is proved in this paper that the agent based fuzzy T-S multi-model system can approximate any linear or nonlinear system at arbitrary accuracy. The applications to the benchmark problem of chaotic time series prediction, water heater system and waste heat utilizing process illustrate the viability and the efficiency of the mentioned approach. At the same time, the method can be easily used to a number of engineering fields, including identification, nonlinear control, fault diagnostics and performance analysis.

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“…In the past few years interval type-2 fuzzy system has been widely studied [25,26,27,28,29]. Adaptive dynamic surface control 50 has been studied in [29].…”

Section: Introductionmentioning

confidence: 99%