SVD-based complexity reduction of rule-bases with nonlinear antecedent fuzzy sets

Takács, Orsolya; Várkonyi-Kóczy, Annamária R.

doi:10.1109/19.997815

Cited by 14 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resulting indices are not too high, so our research results are not applicable for real diagnosis. It is clear, that a further search for efficient antecedents is needed, as well as a systematic optimization of the antecedents should be included [45], or perhaps some bacterial or other nature inspired algorithms, it is worth to start in this direction, and whether the Rényi entropy based quantities are applicable for this purpose.…”

Section: Antecedent Group Antecedentmentioning

confidence: 99%

Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification

Sziová

Nagy

Fazekas

2021

Entropy

View full text Add to dashboard Cite

For finding colorectal polyps the standard method relies on the techniques and devices of colonoscopy and the medical expertise of the gastroenterologist. In case of images acquired through colonoscopes the automatic segmentation of the polyps from their environment (i.e., from the bowel wall) is an essential task within computer aided diagnosis system development. As the number of the publicly available polyp images in various databases is still rather limited, it is important to develop metaheuristic methods, such as fuzzy inference methods, along with the deep learning algorithms to improve and validate detection and classification techniques. In the present manuscript firstly a fuzzy rule set is generated and validated. The former process is based on a statistical approach and makes use of histograms of the antecedents. Secondly, a method for selecting relevant antecedent variables is presented. The selection is based on the comparision of the histograms computed from the measured values for the training set. Then the inclusion of the Rényi-entropy-based structural entropy and the spatial filling factor into the set of input variables is proposed and assessed. The beneficial effect of including the mentioned structural entropy of the entropies from the hue and saturation (H and S) colour channels resulted in 65% true positive and 60% true negative rate of the classification for an advantageously selected set of antecedents when working with HSV images.

show abstract

Section: Antecedent Group Antecedentmentioning

confidence: 99%

Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification

Sziová

Nagy

Fazekas

2021

Entropy

View full text Add to dashboard Cite

show abstract

“…[4], [5], [6], [7] and [7]. Combined with the SVD technique, SC models are excellent tools for anytime operations.…”

Section: Reduction Ofmentioning

confidence: 99%

Anytime modeling: Compression and improvement of the approximation of SC based mappings

Várkonyi-Kóczy¹,

Várkonyi

2009

2009 4th International Symposium on Computational Intelligence and Intelligent Informatics

View full text Add to dashboard Cite

Nowadays practical solutions of engineering problems involve model-integrated computing. Due to their flexibility, robustness, and easy interpretability, the application of soft computing based models, may have an exceptional role. Despite of their advantages, the usage is still limited by their exponentially increasing computational complexity. Although, combining soft computing and anytime techniques it becomes possible to cope with the available, usually imperfect or even missing information, the dynamically changing, possibly insufficient amount of resources and reaction time. In this paper, possibilities offered by (Higher Order) Singular Value Decomposition ((HO)SVD) based anytime Soft Computational (SC) models are analyzed. With the help of SVD, the redundancy of fuzzy systems and neural nets can be removed and further, non-exact reduction can also be obtained. The method also offers a way forimproving the model if later on we get into possession of new information or more resources. An algorithm is suggested, which finds the common minimal implementation space of the compressed original and the new approximation points, thus the two techniques, non-exact complexity reduction and improvement of the approximation accuracy, ensure that we can always cope with the temporarily available (finite) time/resources and find the balance between accuracy and complexity.

show abstract

“…The SVD based complexity reduction can be applied to various types of fuzzy systems, see e.g. Takács et Várkonyi-Kóczy, 2002, andTakács et Várkonyi-Kóczy, 2003.…”

Section: Singular Value Decompositionmentioning

confidence: 99%

Anytime Models in Fuzzy Control

Várkonyi-Kóczy¹,

Bencsik²,

Ruano³

2010

Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics

View full text Add to dashboard Cite

In time critical applications, anytime mode of operation offers a way to ensure continuous operation and to cope with the possibly dynamically changing time and resource availability. Soft Computing, especially fuzzy model based operation proved to be very advantageous in power plant control where the high complexity, nonlinearity, and possible partial knowledge usually limit the usability of classical methods. Higher Order Singular Value Decomposition based complexity reduction makes possible to convert different classes of fuzzy models into anytime models, thus offering a way to combine the advantages, like low complexity, flexibility, and robustness of fuzzy and anytime techniques. By this, a model based anytime control methodology can be suggested which is able to keep on continuous operation using nonexact, approximate models of the plant, thus preventing critical breakdowns in the operation. In this paper, an anytime modeling method is suggested which makes possible to use complexity optimized fuzzy models in control. The technique is able to filter out the redundancy of fuzzy models and can determine the near optimal non-exact model of the plant considering the available time and resources. It also offers a way to improve the granularity (quality) of the model by building in new information without complexity explosion.

show abstract

SVD-based complexity reduction of rule-bases with nonlinear antecedent fuzzy sets

Cited by 14 publications

References 5 publications

Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification

Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification

Anytime modeling: Compression and improvement of the approximation of SC based mappings

Anytime Models in Fuzzy Control

Contact Info

Product

Resources

About