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In this work a new optimization method, called the heuristic Kalman algorithm (HKA), is presented. This new algorithm is proposed as an alternative approach for solving continuous , non-convex optimization problems. The principle of HKA is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum. A(More)
The main objective of this paper is to present a new optimization approach, which we call heuristic Kalman algorithm (HKA). We propose it as a viable approach for solving continuous nonconvex optimization problems. The principle of the proposed approach is to consider explicitly the optimization problem as a measurement process designed to produce an(More)
H ∞ control Robustness Non-convex optimization problems Heuristic Kalman algorithm a b s t r a c t This paper presents a simple but effective tuning strategy for robust PID controllers satisfying multiple H ∞ performance criteria. Finding such a controller is known to be computationally intractable via the conventional techniques. This is mainly due to the(More)
The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parameterization method which is not necessarily based on an iterative training. This approach can be seen as a pre-parameterization which allows the determination of the rules base and the parameters of the membership functions. We also(More)
one can see Eq. (3) is not very simple but it is used a few times for each image and so computing time are not signiicantly changed. Another problem is that now epipolar lines are no more parallel; anyway, if theta is small, one can suppose them to be still parallel and so performing the matching in the way seen before. All of this leads to a decay in(More)