Babak Rezae | European Centre for Soft Computing (original) (raw)

Papers by Babak Rezae

Electronics Letters, 1998

A new cluster validation index is presented which can be used to eliminate the montonically decre... more A new cluster validation index is presented which can be used to eliminate the montonically decreasing tendency when the number of clusters becomes very large and close to the number of data points. The limiting behaviour is described and numerical examples presented to show the effectiveness of the proposed cluster validity index.

2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2014

The conventional Takagi-Sugeno (T-S) fuzzy model is an effective tool used to approximating behav... more The conventional Takagi-Sugeno (T-S) fuzzy model is an effective tool used to approximating behaviors of nonlinear systems on the basis of precise and certain input and output observations. In some situations, however, we can only obtain mixture of precise data (for input variables), imprecise and uncertain data (for output variable/response). This paper presents a method used to constructing T-S fuzzy model in such case where the imprecise and uncertain output observations are represented as fuzzy belief function, and then proposes the so-called mixture data-driven T-S fuzzy model, among which, the consequents are identified by using a novel fuzzy evidential Expectation-Maximization (EM) algorithm and the antecedents are automatically constructed by using a data-driven strategy, considering both the accuracy and complexity of model. The performance of such mixture data data-driven fuzzy model was validated by conducting some unreliable sensor experiments. The numerical simulations suggest that the proposed fuzzy model can be used to approximate nonlinear systems with high accuracy when the outputs of systems are imprecisely and uncertainly observed.

IEEE International Conference on Fuzzy Systems, 2012

Interval type-2 fuzzy sets (IT2 FSs) has been used in wide range of applications, such as decisio... more Interval type-2 fuzzy sets (IT2 FSs) has been used in wide range of applications, such as decision making, pattern recognitions. However, there is little investigation on the clustering techniques of IT2 FSs, since it is very important for measuring the degree of similarity between interval type-2 fuzzy sets in clustering research. In this paper, we give a novel method to

Fuzzy Information, 2004. …, 2004

... Mohammad H. Faze1 Zarandi Ismail B.Tiir!qen Babak Rezaee Deparlment ofhdusirial Deparlment of... more ... Mohammad H. Faze1 Zarandi Ismail B.Tiir!qen Babak Rezaee Deparlment ofhdusirial Deparlment of Mechanical and Department of Indusirial ... Box: 15875-4413 M5S3G8 Box: 15875-4413 zarandi@aut.ac.ir turksen@mie.utoronto.ca Babak-Rezaee@aut.ac.ir ...

Fuzzy Sets and Systems, 2010

In this paper, a cluster validity index proposed by Kim et al. [15] is analyzed, and a problem is... more In this paper, a cluster validity index proposed by Kim et al. [15] is analyzed, and a problem is discussed that the validity index faces in situations when there are well-separated clusters that themselves include subclusters. Based on this analysis, a new validity index is proposed. ...

Different similarity measures for type-2 fuzzy sets have been proposed in the literature. However... more Different similarity measures for type-2 fuzzy sets have been proposed in the literature. However, there are not analytical formulas for them. In this paper, an extension of Jaccard's similarity measure for type-2 fuzzy sets is considered and the generalized formula in analytical form for the similarity measure of two interval fuzzy sets with Gaussian Primary membership function is derived.

It is known that type-2 fuzzy sets let us to model and to minimize the effects of uncertainties i... more It is known that type-2 fuzzy sets let us to model and to minimize the effects of uncertainties in rule-based fuzzy logic system (FLS). While a type-2 FLS has the capability to model more complex relationships, the output of a type-2 fuzzy inference engine needs to be type-reduced. As type-reduction is very computationally intensive, type-2 FLSs may not be suitable for certain real-time applications. This paper aims at developing more computationally efficient output processing consists of type-reduction followed by defuzzification. The type-reduced set is approximated by linear combinations of the inner- and outer-bound sets for the type-reduced set and also the crisp output of type-2 FLS is computed by another. Parameters of these functions are determined during the training phase. By this approach type-2 FLSs can handle such uncertainties in a better way because they provide us with more parameters and more design degrees of freedom. Simulation is presented to demonstrate that the proposed type-reducing and defuzzification algorithms have lower computational cost and better performances than the Karnik-Mendel and Wu-Mendel algorithms.

In this paper, a type-2 Fuzzy Rule Based Expert System is developed for analysing the stock marke... more In this paper, a type-2 Fuzzy Rule Based Expert System is developed for analysing the stock markets. Interval type-2 fuzzy logic system permits us to model rule uncertainties and every membership value of an element is interval itself. The proposed type-2 fuzzy model applies the technical and fundamental indexes as the input variables. The fuzzy rule based model is tested on the stock market of an automotive manufactory in Asia. The proposed model can forecast the stock price variation. This can help the investors the select the best portfolio.

Expert Systems With Applications, 2009

In this paper, a type-2 fuzzy rule based expert system is developed for stock price analysis. Int... more In this paper, a type-2 fuzzy rule based expert system is developed for stock price analysis. Interval type-2 fuzzy logic system permits us to model rule uncertainties and every membership value of an element is interval itself. The proposed type-2 fuzzy model applies the technical and fundamental indexes as the input variables. This model is tested on stock price prediction of an automotive manufactory in Asia. Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks from different sectors. The results are very encouraging and can be implemented in a real-time trading system for stock price prediction during the trading period.

International Journal of Advanced Manufacturing Technology, 2010

Two Takagi–Sugeno–Kang fuzzy models for the prediction of the amount of reagents for desulfurizat... more Two Takagi–Sugeno–Kang fuzzy models for the prediction of the amount of reagents for desulfurization in steel processing are developed from experimental data. For the design of the models, an algorithm was proposed to be used in the procedures of the two phases: structure building and parametric identification. In the first phase, the Gustafson–Kessel clustering algorithm with the cluster validity index was proposed to find the number of fuzzy rules and an initial fuzzy model. In the second phase, a gradient-descent-based approach was developed and used for optimized tuning of membership functions of the fuzzy model. The numerical results were compared with a conventional statistical model and neural networks and adaptive network-based fuzzy inference system.

Eighth International Conference on Hybrid Intelligent …, Jan 1, 2008

This paper addresses a new approach for automatically extraction of the type-2 fuzzy rules from i... more This paper addresses a new approach for automatically extraction of the type-2 fuzzy rules from input-output data. In this approach, the structure identification and parameter optimization are carried out automatically without any assumption about the structure of the data, which is ...

Fifth International Conference on Fuzzy …, Jan 1, 2008

In this paper a new fuzzy modeling approach is proposed, which is devoted to discover knowledge f... more In this paper a new fuzzy modeling approach is proposed, which is devoted to discover knowledge from data and represent it in the form of fuzzy rules. In this approach, the structure identification and parameter optimization are carried out automatically without any assumption about ...

Fuzzy Sets and Systems, Jan 1, 2010

Information Sciences, Jan 1, 2010

This paper presents a systematic approach to design first order TagakiSugenoKang (TSK) fuzzy sy... more This paper presents a systematic approach to design first order TagakiSugenoKang (TSK) fuzzy systems. This approach attempts to obtain the fuzzy rules without any assumption about the structure of the data. The structure identification and parameter optimization steps in ...

Fuzzy Information, 2004. …, Jan 1, 2004

Expert Systems with Applications, Jan 1, 2009