An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models

Chen, Chao; John, Robert; Twycross, Jamie; Garibaldi, Jonathan M.

doi:10.1109/fuzz-ieee.2016.7737742

Cited by 17 publications

(14 citation statements)

References 16 publications

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“…Specifically, on the low and medium voltage electricity data, all the training and testing RMSE values of AT2-SCRATCH models are larger than those of AT1-SCRATCH. This behaviour is consistent with that described in [9].…”

Section: ) Datasupporting

confidence: 93%

“…In this paper, the architecture of T1 and IT2 ANFIS models is based on the work in [9]. Specifically, the antecedent membership functions of our T1 ANFIS models are based on the generalised bell-shaped function defined as:…”

Section: Anfis Optimisationmentioning

confidence: 99%

“…However, in practice, it should be noted that IT2 FLS does not always provide significant improvement compared to T1 FLS. In fact, IT2 FLS can in some cases be worse than T1 FLS if they are both optimised from scratch [9].…”

Section: Introductionmentioning

confidence: 99%

“…In a previous study [9], we have shown that the least-square-estimate (LSE) method together with the Karnik-Mendel (KM) algorithm behaves differently for interval IT2 ANFIS compared to T1 ANFIS. And in that paper, IT2 ANFIS produced generally larger root-meansquared-error (RMSE) than T1 ANFIS on the well-known IRIS classification problem.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

John

Twycross

et al. 2017

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

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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.

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Section: ) Datasupporting

confidence: 93%

Section: Anfis Optimisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen

John

Twycross

et al. 2017

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

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“…There are several established approaches to tune FLSs using statistical optimisation. Here, ANFIS (adaptive-network-based fuzzy inference system), introduced by Jang [14], and later extended in [6] for interval type-2 fuzzy logic system has been one of the most popular. ANFIS uses statistical optimisation to update FLS parameters based on a given training dataset with the objective to deliver good performance, i.e.…”

Section: Introductionmentioning

confidence: 99%

Performance and Interpretability in Fuzzy Logic Systems – Can We Have Both?

Pekaslan

Chen

Wagner

et al. 2020

Communications in Computer and Information Science

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Fuzzy Logic Systems can provide a good level of interpretability and may provide a key building block as part of a growing interest in explainable AI. In practice, the level of interpretability of a given fuzzy logic system is dependent on how well its key components, namely, its rule base and its antecedent and consequent fuzzy sets are understood. The latter poses an interesting problem from an optimisation point of view -if we apply optimisation techniques to optimise the parameters of the fuzzy logic system, we may achieve better performance (e.g. prediction), however at the cost of poorer interpretability. In this paper, we build on recent work in non-singleton fuzzification which is designed to model noise and uncertainty 'where it arises', limiting any optimisation impact to the fuzzification stage. We explore the potential of such systems to deliver good performance in varying-noise environments by contrasting one example framework -ADONiS, with ANFIS, a traditional optimisation approach designed to tune all fuzzy sets. Within the context of time series prediction, we contrast the behaviour and performance of both approaches with a view to inform future research aimed at developing fuzzy logic systems designed to deliver both -high performance and high interpretability.

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