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Articles by M.B. Menhaj
Total Records ( 3 ) for M.B. Menhaj
  S. Jafari , H.R. Abdolmohammadi , H. Eliasi , M.B. Menhaj and M.R. Rajati
  The aim of this study is to provide an experiment design method for modeling and function approximation. Modeling real-life systems is extremely of interest nowadays. Models could be useful in analysis of systems and help us understand their behavior. From a new point, models could be classified into three classes: black box models, gray box models and white box models. Our idea is related to black box modeling. Proper performance of a black box model depends on structure of the model as well as the data used to determine its parameters. Although one of the important factors affecting the richness of the dataset is the number of data, increasing the number of data points is limited in real problems. For instance gathering data from many systems imposes spending lots of time and cost. In this study, inspired by honey bee algorithm, we have designed a method which enriches the datasets for a known number of data, in comparison to other conventional data extraction methods. In such a method, after extracting some data by grid method, the other data points are extracted according to an intelligent analysis on available data. The results illustrate the efficiency of the proposed method.
  R. Ghasemi , M.B. Menhaj and A. Afshar
  A new method to design a decentralized Fuzzy Adaptive Controller (FAC) for a class of large scale nonaffine nonlinear systems is proposed in this study. It is assumed that functions of the subsystems and their interactions are unknown. To design controller, the lyapunov function is proposed for the system and then unknown parameters of controller and system are derived based on the stability theory. The robustness against uncertainty and external disturbance, the boundedness of the estimation errors, the convergence of the output error to zero and the lyapunov stability of the closed loop system are guaranteed. To use the knowledge of the experts in FAC is another advantage of controller. Robust adaptive control has been used to avoid chattering in adaptation laws. An illustrative example is given to show the promising performance of the proposed method.
  B. Safarinejadian , M.B. Menhaj and M. Karrari
  In this study, a distributed expectation maximization (DEM) algorithm is first introduced in a general form for estimating parameters of a finite mixture of components. This algorithm is used for density estimation and clustering of the data distributed over the nodes of a network. Then, a distributed incremental EM algorithm (DIEM) with a higher convergence rate is proposed. After a full derivation of distributed EM algorithms, convergence of both DEM and DIEM algorithms is studied based on the negative free energy concept. It is shown that these algorithms increase the negative free energy incrementally at each node until reaching the convergence. Finally, the proposed algorithms are applied to cluster analysis of gene-expression data. Simulation results approve that DIEM remarkably outperforms DEM.
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