Evolutionary Extreme Learning Machine for the Interval Type-2 Radial Basis Function Neural Network: A Fuzzy Modelling Approach

Rubio-Solis, Adrian; Martinez-Hernandez, Uriel; Panoutsos, George

doi:10.1109/fuzz-ieee.2018.8491583

Cited by 13 publications

(18 citation statements)

References 31 publications

(45 reference statements)

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“…to be a faster learning method aiming to solve the heavy computational burden that results from learning methods such as gradient descent and quadratic programming. ML-ELM implementations have also shown superb efficiency in a number of different applications such as image processing [26], pattern recognition [17,27,28], complex systems modelling [29] and human-centered computing [6].…”

Section: Compared To Deep Learning Multilayer Extreme Learning Machine (Ml-elm) Has Demonstratedmentioning

confidence: 99%

“…Most of the applications and design of hybrid neural network systems whose structure is based on the theory of Interval Type-2 Fuzzy Logic (IT2 FL) and Extreme Learning Machine (ELM) have been concentrated on solving regression problems. Such systems usually employed Multiple-Input-Single-Output (MISO) neural structures with a Karnik-Mendel type-reduction layer [29,37,38]. In this paper, the final layer of the ML-IT2-FELM is an IT2-FELM whose main inference engine is based on the model of a Multi-Input-Multi-Output (MIMO) IT2-RBFNN [27].…”

Section: Interval Type-2 Fuzzy Extreme Learning Machine For Classification (It2-felm)mentioning

confidence: 99%

“…As pointed out in , in order to consider a of system IT2, only one out of its components must be of IT2. Within this context, the Thereby, in order to find the parameters of the MIMO IT2-RBFNN, ELM is systematically called in two different steps in order to update the consequent weights in the IT2-RBFNN output layer [29,37]. At first step [37], the optimal initial values for the consequents are obtained by approximating the reduced set for the sth output [y s l , y s r ] as:…”

Section: Interval Type-2 Fuzzy Extreme Learning Machine For Classification (It2-felm)mentioning

confidence: 99%

“…In the second phase, the final hidden layer L is an IT2-FELM whose input is X (i+1) . As described in the previous section, in order to find the parameters of the IT2-FELM, ELM is systematically called in two different steps in order to update the consequent weights in the IT2-FELM output layer [29,37].…”

Section: Hierarchical Ml-it2-felmmentioning

confidence: 99%

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A Multilayer Interval Type-2 Fuzzy Extreme Learning Machine for the recognition of walking activities and gait events using wearable sensors

et al. 2020

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In this paper, a novel Multilayer Interval Type-2 Fuzzy Extreme Learning Machine (ML-IT2-FELM) for the recognition of walking activities and Gait events is presented. The ML-IT2-FELM uses a hierarchical learning scheme that consists of multiple layers of IT2 Fuzzy Autoencoders (FAEs), followed by a final classification layer based on an IT2-FELM architecture. The core building block in the ML-IT2-FELM is the IT2-FELM, which is a generalised model of the Interval Type-2 Radial Basis Function Neural Network (IT2-RBFNN) and that is functionally equivalent to a class of simplified IT2 Fuzzy Logic Systems (FLSs). Each FAE in the ML-IT2-FELM employs an output layer with a direct-defuzzification process based on the Nie-Tan algorithm, while the IT2-FELM classifier includes a Karnik-Mendel type-reduction method (KM). Real data was collected using three inertial measurements units attached to the thigh, shank and foot of twelve healthy participants. The validation of the ML-IT2-FELM method is performed with two different experiments. The first experiment involves the recognition of three different walking activities: Level-Ground Walking (LGW), Ramp Ascent (RA) and Ramp Descent (RD). The second experiment consists of the recognition of stance and swing phases during the gait cycle. In addition, to compare the efficiency of the ML-IT2-FELM with other ML fuzzy methodologies, a kernel-based ML-IT2-FELM that is inspired by kernel learning and called KML-IT2-FELM is also implemented. The results from the recognition of walking activities and gait events achieved an average accuracy of 99.98% and 99.84% with a decision time of 290.4ms and 105ms, respectively, by the ML-IT2-FELM, while the KML-IT2-FELM achieved an average accuracy of 99.98% and 99.93% with a decision time of 191.9ms and 94ms. The experiments demonstrate that the ML-IT2-FELM is not only an effective Fuzzy Logic-based approach in the presence of sensor noise, but also a fast extreme learning machine for the recognition of different walking activities.

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Section: Compared To Deep Learning Multilayer Extreme Learning Machine (Ml-elm) Has Demonstratedmentioning

confidence: 99%

Section: Interval Type-2 Fuzzy Extreme Learning Machine For Classification (It2-felm)mentioning

confidence: 99%

Section: Interval Type-2 Fuzzy Extreme Learning Machine For Classification (It2-felm)mentioning

confidence: 99%

Section: Hierarchical Ml-it2-felmmentioning

confidence: 99%

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A Multilayer Interval Type-2 Fuzzy Extreme Learning Machine for the recognition of walking activities and gait events using wearable sensors

et al. 2020

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“…After structure identification, the common parameters m is applied [37]. For each p input-output training data ( x p , d p ); p = 1, .…”

Section: B Adaptive Gradient Descent Approach (Agd)mentioning

confidence: 99%

General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model

Rubio-Solis

Melín

Martinez-Hernandez

et al. 2019

IEEE Trans. Fuzzy Syst.

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This paper proposes a new General Type-2 Radial Basis Function Neural Network (GT2-RBFNN) that is functionally equivalent to a GT2 Fuzzy Logic System (FLS) of either Takagi-Sugeno-Kang (TSK) or Mamdani type. The neural structure of the GT2-RBFNN is based on the α-planes representation, in which the antecedent and consequent part of each fuzzy rule uses GT2 Fuzzy Sets (FSs). To reduce the iterative nature of the Karnik-Mendel algorithm, the Enhaned-Karnik-Mendel (EKM) type-reduction and three popular direct-defuzzification methods, namely the 1) Nie-Tan approach (NT), the 2) Wu-Mendel uncertain bounds method (WU) and the 3) Biglarbegian-Melek-Mendel algorithm (BMM) are employed. For that reason, this paper provides four different neural structures of the GT2-RBFNN and their structural and parametric optimisation. Such optimisation is a two-stage methodology that first implements an Iterative Information Granulation approach to estimate the antecedent parameters of each fuzzy rule. Secondly, each consequent part and the fuzzy rule base of the GT2-RBFNN is trained and optimised using an Adaptive Gradient Descent method (AGD) respectively. Several benchmark data sets, including a problem of identification of a nonlinear system and a chaotic time series are considered. The reported comparative analysis of experimental results is used to evaluate the performance of the suggested GT2 RBFNN with respect to other popular methodologies.

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A snapshot audit of global flexible endoscopy practice among European Association of Endoscopic Surgeons (EAES) and Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) surgeons from the EAES Flexible Endoscopy Subcommittee survey

Rodríguez-Luna,

Keller,

Guerriero

et al. 2024

Surg Endosc

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Evolutionary Extreme Learning Machine for the Interval Type-2 Radial Basis Function Neural Network: A Fuzzy Modelling Approach

Abstract: This is a repository copy of Evolutionary extreme learning machine for the interval type-2 radial basis function neural network: A fuzzy modelling approach.

Cited by 13 publications

References 31 publications

A Multilayer Interval Type-2 Fuzzy Extreme Learning Machine for the recognition of walking activities and gait events using wearable sensors

A Multilayer Interval Type-2 Fuzzy Extreme Learning Machine for the recognition of walking activities and gait events using wearable sensors

General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model

A snapshot audit of global flexible endoscopy practice among European Association of Endoscopic Surgeons (EAES) and Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) surgeons from the EAES Flexible Endoscopy Subcommittee survey

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