Fuzzy system modeling approximates highly nonlinear systems by means of fuzzy if-then rules. In the literature, different approaches are proposed for mining fuzzy if-then rules from historical data. These approaches usually utilize fuzzy clustering in structure identification phase. In this research, we are going to analyze three possible approaches from the literature and try to compare their ...

In this paper a novel algorithm is proposed that integrates the process of fuzzy hierarchy generation and rule discovery for automated discovery of Production Rules with Fuzzy Hierarchy (PRFH) in large databases. A concept of frequency matrix (Freq) introduced to summarize large database that helps in minimizing the number of database accesses, identification and removal of irrelevant attribute...

The paper offers evolving Takagi–Sugeno (T–S) fuzzy models for a nonlinear benchmark represented by the pendulum-crane system and focused on the dynamics of pendulum angular position behavior. The rule bases and parameters of T–S fuzzy models are continuously evolved by an online identification algorithm in terms of computing the potentials of new data points. Accepting the pendulum angle as mo...

In this paper, we present a novel technique to analysis fingerprint image quality using fuzzy logic. The quality of fingerprint image greatly affects the performance of minutiae extraction and the process of matching in fingerprint identification system. The system uses the extracted four features from a fingerprint image which are the Local Clarity Score (LCS), Global Clarity Score (GCS), Ridg...

Abstract – This paper investigates the identification of nonlinear systems by neural networks. As the identification methods, Feedforward Neural Networks (FNN), Radial Basis Function Neural Networks (RBFNN), Runge-Kutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identification mechanisms are studied and their performances are comparatively evaluated on a thr...

Abstract. This paper presents a theoretical approach for identifying the dimensionless mean coefficient of participating fuzzy mass which is the main unknown parameter of the type I or II fuzzy law previously introduced by the author. This method is based on the use of the associated power flow equation, each power term being identified by using a global statistical energy analysis of the fuzzy...

Exploring the growing interest in extending the theory of probability and statistics to allow for more flexible modeling of uncertainty, ignorance, and fuzziness, the properties of fuzzy modeling are investigated for statistical signals, which benefit from the properties of fuzzy modeling. There is relatively research in the area, making explicit identification of statistical/stochastic fuzzy m...

Motivated by the need to develop more effective methods of controlling uncertain non-linear systems, this paper focus on developing an adaptive fuzzy model-based controller, in which the optimisation variables remain in the fuzzy domain. The scheme uses an on-line fuzzy identification scheme, which is able to generate a fuzzy relational model using the training data from the system. The propose...

recent advances in wireless and mobile communications infrastructure are enabling governments to deliver and manage services to citizen efficiently and economically. mobile government provides this ability to governments. this paper presents the ggovernance approach in effective development of mobile government. the objective is reviews(identify and ranking) of public service concept (included:...

This paper proposes a method to estimate the a priori probability for speakers based on the training data set, speaker models and a fuzzy estimation technique. Speaker identification experiments performed on 138 Gaussian mixture speaker models in the YOHO database using the priors estimated by the fuzzy estimation method showed lower error rates than using those estimated by the probabilistic e...