Evolving neural network with extreme learning for system modeling

Rosa, Raul Arthur Fernandes; Gomide, Fernando; Dovzan, Djan; Škrjanc, Igor

doi:10.1109/eais.2014.6867468

Cited by 23 publications

(9 citation statements)

References 25 publications

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“…36. The difference compared to the previous example is that here the parameters a, b and c are not fixed, but change over time according to the equations in [58], where the detailed description of experiment is also given.…”

Section: E Comparison Of the Efumo To Other Similar Methodsmentioning

confidence: 96%

“…The two tables demonstrate two different experimental setups. In Table III the experiment is setup as described in [58]. All the data are used for testing [59] 10 0.0129 RANEKF [60] 11 0.0184 simpl eTS [61] 18 0.0122 eTS [29] 19 0.0082 SONFIN [25] 10 0.013 SAFIN [57] 13 0.007 eFuMo [48] 12 0.0035 and validation.…”

Section: E Comparison Of the Efumo To Other Similar Methodsmentioning

confidence: 99%

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Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process

Dovžan

Logar

Škrjanc

2015

IEEE Trans. Fuzzy Syst.

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134

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Increasing demands on effluent quality and loads call for an improved control, monitoring and fault detection of waste-water treatment plants (WWTPs). Improved control and optimization of WWTP lead to increased pollutant removal, a reduced need for chemicals as well as energy savings. An important step towards the optimal functioning of a WWTP is to minimize the influence of sensor faults on the control quality. To achieve this a fault-detection system should be implemented. In this paper the idea of using an evolving method as a base for the faultdetection/monitoring system is tested. The system is based on the evolving-fuzzy-model method. This method allows us to model the nonlinear relations between the variables with the Takagi-Sugeno fuzzy model. The method uses basic evolving mechanisms to add and remove clusters and the adaptation mechanism to adapt the clusters' and local models' parameters. The proposed faultdetection system is tested on measured data from a real WWTP. The results indicate the potential improvement of the WWTP's control during a sensor malfunction.

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Section: E Comparison Of the Efumo To Other Similar Methodsmentioning

confidence: 96%

Section: E Comparison Of the Efumo To Other Similar Methodsmentioning

confidence: 99%

Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process

Dovžan

Logar

Škrjanc

2015

IEEE Trans. Fuzzy Syst.

Self Cite

134

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show abstract

“…where the index k again denotes the class index k = 1, ...,C. z t denotes the regressor vector of the current sample, η is the current kalman gain vector, I L t s is an identity matrix based on the number of neurons in the second layer, L t s × L t s ; ψ ∈]0, 1] denotes forgetting factor (1 per default). Q denotes the inverse Hessian matrix Q = (Z T sel Z sel ) −1 and is set initially as ωI L t s , where ω = 1000 [25]. This matrix is directly and incrementally updated by the second equation above without requiring (time-consuming and possibly unpredictable) matrices re-inversion.…”

Section: Training and Model Interpretabilitymentioning

confidence: 99%

Evolving Fuzzy Neural Network Based on Uni-nullneuron to Identify Auction Fraud

Souza

Lughofer

Guimarães³

2021

Atlantis Studies in Uncertainty Modelling

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The increase in transactions on the Internet related to the purchase of products or services can provide facilities for the parties involved in these acquisitions, but they also generate uncertainties and possibilities of attacks that can originate from fraud. This work seeks to explore and extract knowledge of auction fraud by using an evolving fuzzy neural network model based on n-uninorms. This new model uses a fuzzification technique based on Typicality and Eccentricity Data Analysis operators and a parallel processor for stream samples. To test the model in solving auction fraud problems, stateof-the-art neuro-fuzzy models were used to compare a public dataset on the topic. The results of the model proposed in this paper were superior to the other models evaluated (close to 96% accuracy) in the test, and the fuzzy rules demonstrate the model's ability to extract knowledge.

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“…

denotes the regressor (row) vector of the current sample (i.e., the activation levels of all neurons in the current stream sample), J is the current kalman gain (row) vector and

is an identity matrix based on the number of neurons in the second layer,

;

denotes a possible forgetting factor, but is to 1 per default (no forgetting). Q denotes the inverse Hessian matrix

and is set initially as

, where

is a big number (e.g., 1000), such as in Rosa et al [ 36 ]; see also Chapter 2 in [ 37 ] for a detailed convergence analysis. This matrix is directly and incrementally updated by the second equation above without requiring (time-consuming and possibly unpredictable) re-inversion of matrices.…”

Section: Evolving Fuzzy Neural Network and Our Approach: Efnn-lnmentioning

confidence: 99%

Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network

Souza

Lughofer

2020

Sensors

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Heart problems are responsible for the majority of deaths worldwide. The use of intelligent techniques to assist in the identification of existing patterns in these diseases can facilitate treatments and decision making in the field of medicine. This work aims to extract knowledge from a dataset based on heart noise behaviors in order to determine whether heart murmur predilection exists or not in the analyzed patients. A heart murmur can be pathological due to defects in the heart, so the use of an evolving hybrid technique can assist in detecting this comorbidity team, and at the same time, extract knowledge through fuzzy linguistic rules, facilitating the understanding of the nature of the evaluated data. Heart disease detection tests were performed to compare the proposed hybrid model’s performance with state of the art for the subject. The results obtained (90.75% accuracy) prove that in addition to great assertiveness in detecting heart murmurs, the evolving hybrid model could be concomitant with the extraction of knowledge from data submitted to an intelligent approach.

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Evolving neural network with extreme learning for system modeling

Cited by 23 publications

References 25 publications

Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process

Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process

Evolving Fuzzy Neural Network Based on Uni-nullneuron to Identify Auction Fraud

Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network

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