Mohammad Mehdi Korjani

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Fuzzy Set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It was developed by the social scientist Prof. Charles C. Ragin. fsQCA seeks to establish logical connections between combinations of causal conditions and an outcome, the result being rules that describe how(More)
The main contribution of this paper is to develop a Perceptual Computer for Fuzzy Love Selection problem. This is a problem of ranking all members (alternatives) in an individual list in order of preference. Uncertainty of the individual about criteria scores and weights assigned to each alternative is handled by means of Perceptual Computer. This paper(More)
Recently, several recurrent neural networks for solving constraint optimization problems were developed. In this paper, we propose a novel approach to the use of a projection neural network for solving real time identification and control of time varying systems. In addition to low complexity and simple structure, the proposed neural network can solve wider(More)
Fuzzy set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It contains 13 steps which are mathematically described in [2]. In this paper we focus on the validation of fsQCA by using it to obtain a granular (linguistic) description of a function as a collection of(More)
Fuzzy set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It has recently been described as a collection of 13 steps [3]. In this paper we focus on how to speed up some of the computationally intensive steps of fsQCA and how to use the speed-up equations to obtain(More)
In this paper, relative capacity of a specific higher order Hopfield-type associative memory is considered. This model, which is known as exponential Hopfield neural network is suitable for hardware implementation and is not of a great computational cost. It is shown that, this modification of the Hopfield model significantly improves the storage capacity(More)
In this paper, we present a modular neural network for learning formation strategy in multi-agent systems. A supervised learning method is devised to train the modular neural network in order for a group of agents to learn formation strategy in an environment. At first, the environment conditions are separated into some different parts called contexts in(More)