TSK fuzzy model with minimal parameters

Guenounou, Ouahib; Dahhou, B.; Chabour, Ferhat

doi:10.1016/j.asoc.2015.02.017

Cited by 16 publications

(6 citation statements)

References 20 publications

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“…From the table, it is clearly shown that the performances including the number of neurons, the number of parameters, RMSE for testing based on our approach are much better than the other existing methods. From Table 1, firstly, it is clearly shown that, in comparing with the existing methods and results, the FNN obtained by our approach is the most accurate one with the testing RMSE as 0.0225, which is less than half of the testing RMSE 0.05 given in [32] as the most accurate existing result in the literature; secondly, the FNN from our approach is the simplest and most compact one. In comparing with the most accurate existing model given in [32], our FNN with 5 neurons and 35 parameters are significantly less than that one with 7 neurons and 37 parameters.…”

Section: Simulation Experimentsmentioning

confidence: 72%

“…From Table 1, firstly, it is clearly shown that, in comparing with the existing methods and results, the FNN obtained by our approach is the most accurate one with the testing RMSE as 0.0225, which is less than half of the testing RMSE 0.05 given in [32] as the most accurate existing result in the literature; secondly, the FNN from our approach is the simplest and most compact one. In comparing with the most accurate existing model given in [32], our FNN with 5 neurons and 35 parameters are significantly less than that one with 7 neurons and 37 parameters. In comparing with the other simplest FNN given in [34], our FNN has the same numbers of neurons and parameters, our testing RMSE as 0.0225 is less than one-seventh of the testing RMSE 0.16 for that FNN obtained in [34]; thirdly, the FNN obtained by our approach has no redundant or highly similar fuzzy rules, which shows that our structure initialization method sometimes can obtain very simple and accurate FNN even without the simplification phase, and therefore confirms the effectiveness of our method; fourthly, the training and testing NRMSEs for the obtained FNN are 0.01 and 0.0047 respectively, which are very close to 0 and therefore further confirm the accuracy of the obtained FNN by the proposed approach.…”

Section: Simulation Experimentsmentioning

confidence: 72%

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Identification of fuzzy neural networks by forward recursive input-output clustering and accurate similarity analysis

Qiao

Zeng

et al. 2016

Applied Soft Computing

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The first phase is the system identification which includes a novel forward recursive input-output clustering (FRIOC) method for the structure initialization and the gradient descent algorithm for the parameter initialization. (2) The second phase is the system simplification which includes the accurate similarity analysis and merging method for similar fuzzy rules and the gradient descent algorithm for the parameter finalization. Parameter initialisation by gradient descent algorithm Similarity calculation No Similarity based merger Yes Similarity matrix Structure initialisation by forward recursive inputoutput clustering approach Structure simplification by similarity based merger method Parameter finalization by gradient descent algorithm t S S  max Structure simplification by identifying and removing redundant rules Initial Mamdani fuzzy neural network The final Mamdani fuzzy neural network

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Section: Simulation Experimentsmentioning

confidence: 72%

Section: Simulation Experimentsmentioning

confidence: 72%

Identification of fuzzy neural networks by forward recursive input-output clustering and accurate similarity analysis

Qiao

Zeng

et al. 2016

Applied Soft Computing

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show abstract

“…T-S fuzzy system can provide a reasonable framework for modeling by decomposition of a nonlinear system into a collection of local linear models. And it is one of the most used because of its good results in different areas and due to its mathematical treatability [ 29 ].…”

Section: Background and Methodsmentioning

confidence: 99%

“…collection of local linear models. And it is one of the most used because of its good results in different areas and due to its mathematical treatability [29].…”

Section: Plos Onementioning

confidence: 99%

Rule-based expert system to assess caving output ratio in top coal caving

et al. 2020

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Coal mining professionals in coal mining have recognized that the assessment of top coal release rate can not only improve the recovery rate of top coal, but also improve the quality of coal. But the process was often performed using a manual-based operation mode, which intensifies workload and difficulty, and is at risk of human errors. The study designs a assessment system to give the caving output ratio in top coal caving as accurately as possible based on the parameters adaptive Takagi-Sugeno (T-S) fuzzy system and the Levenberg-Marquardt (LM) algorithm. The main goal of the adaptive parameters based on LM algorithm is to construct its damping factor in the light of lowering of the objective function which is as taken as the index of termination iteration. The performance of the system is evaluated by Pearson correlation coefficient, Coefficient of Determination and relative error where the results of the Takagi-Sugeno method and the parameters adaptive Takagi-Sugeno method are compared to make the evaluation more robust and comprehensive.

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“…To formalize the application of ML methods, the ANFIS design can be considered a multi-layered connectionist framework, as represented in figure 4.2. The model's knowledge base uses Takagi and Sugeno's [34] fuzziness models. Fuzzy sets correspond to both crucial and regular input feature variables, and fuzzy rules are made by linearly combining a constant and a data object.…”

Section: Nfmentioning

confidence: 99%

An AI-Based Classification and Recommendation System for Digital Libraries

Alomran,

Basha

2024

SCPE

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The immense volume of online content linked to digital libraries has given emergence to the advancement of screening and recommendation systems. A recommendation system is vitally important in both academic institutions and elibraries to assist professors, instructors, students, and researchers in finding appropriate sources of information. Distributed or collaborative screening is the most common method used in current recommendation systems. However, collaborative approaches cannot promote library repositories, including unrated or unpurchased electronic information. Thus, this paper deals with the automated classification and recommendation of a multiclass corpus found in virtual repositories (cloud databases). In various stages, Neuro-Fuzzy (NF) and Support Vector Machine (SVM) techniques are used as the base classifiers for the categorization of the essential subjects (contents). Later, a high-level ensemble learning strategy is utilized to recommend appropriate subjects from the available multiclass corpus. The methods use a CoC (Coherence of Content)-based inference mechanism to extract and filter the critical components before beginning the recommendation process. Experiments demonstrated that a recommended approach based on detailed conceptual descriptions instead of a handful of phrases/words might help academic and research communities to find relevant sources. Observing the results over a period of months shows that the suggested method increases user comfort, proving the system’s acceptability to users in this way. In addition, compared to previous models, the accuracy in categorizing therequisite subjects is more than 97.16 per cent.

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TSK fuzzy model with minimal parameters

Cited by 16 publications

References 20 publications

Identification of fuzzy neural networks by forward recursive input-output clustering and accurate similarity analysis

Identification of fuzzy neural networks by forward recursive input-output clustering and accurate similarity analysis

Rule-based expert system to assess caving output ratio in top coal caving

An AI-Based Classification and Recommendation System for Digital Libraries

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