Rule-by-rule input significance analysis in fuzzy system modeling

Date

2004-06-30

Author

Uncu, O
Turksen, IB

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

90
views

0
downloads

Input or feature selection is one the most important steps of system modeling. Elimination of irrelevant variables can save time, money and can improve the precision of model that we are trying to discover. In Fuzzy System Modeling (FSM) approaches, input selection plays an important role too. The input selection algorithms that are under our investigation did not consider one crucial fact. An input variable may of may not be significant in a specific rule, not in overall system. In this paper, an input selection algorithm that takes this observation into account is proposed as an extension of the input selection algorithms found in the literature. The proposed algorithm is applied on a nonlinear function and successful results are achieved.

Subject Keywords

Neural-network, Selecition

URI

https://hdl.handle.net/11511/66157

Collections

Department of Industrial Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Texture classification and retrieval using random neural network model Teke, Alper; Atalay, Mehmet Volkan; Department of Computer Engineering (2003) Texture is one of the most important characteristics used in computer vision and image processing applications. In this thesis, a new texture classification and retrieval method is proposed for texture analysis applications. The technique makes use of the random neural network model and it is supervised. The main aim is to represent textures with parameters which are the random neural network weights and classify and retrieve textures using this texture definition. The network has neurons that correspond to...
Identifying (Quasi) Equally Informative Subsets in Feature Selection Problems for Classification: A Max-Relevance Min-Redundancy Approach Karakaya, Gülşah; AHİPAŞAOĞLU, Selin Damla; TAORMİNA, Riccardo (2016-06-01) An emerging trend in feature selection is the development of two-objective algorithms that analyze the tradeoff between the number of features and the classification performance of the model built with these features. Since these two objectives are conflicting, a typical result stands in a set of Pareto-efficient subsets, each having a different cardinality and a corresponding discriminating power. However, this approach overlooks the fact that, for a given cardinality, there can be several subsets with sim...
A New Design Approach for Rapid Evaluation of Structural Modifications Using Neural Networks Demirkan, O.; Olceroglu, E.; BAŞDOĞAN, FATMA İPEK; Özgüven, Hasan Nevzat (2013-02-01) Design optimization of structural systems is often iterative, time consuming and is limited by the knowledge of the designer. For that reason, a rapid design optimization scheme is desirable to avoid such problems. This paper presents and integrates two design methodologies for efficient conceptual design of structural systems involving computationally intensive analysis. The first design methodology used in this paper is structural modification technique (SMT). The SMT utilizes the frequency response funct...
Comparison of two inference approaches in Gaussian graphical models Purutçuoğlu Gazi, Vilda; Wit, Ernst (Walter de Gruyter GmbH, 2017-04-01) Introduction: The Gaussian Graphical Model (GGM) is one of the well-known probabilistic models which is based on the conditional independency of nodes in the biological system. Here, we compare the estimates of the GGM parameters by the graphical lasso (glasso) method and the threshold gradient descent (TGD) algorithm.
Visual object detection and tracking using local convolutional context features and recurrent neural networks Kaya, Emre Can; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2018) Visual object detection and tracking are two major problems in computer vision which have important real-life application areas. During the last decade, Convolutional Neural Networks (CNNs) have received significant attention and outperformed methods that rely on handcrafted representations in both detection and tracking. On the other hand, Recurrent Neural Networks (RNNs) are commonly preferred for modeling sequential data such as video sequences. A novel convolutional context feature extension is introduc...

Citation Formats

O. Uncu and I. Turksen, “Rule-by-rule input significance analysis in fuzzy system modeling,” 2004, p. 931, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66157.