the infomax algorithm is a popular method in blind source separation problem. in this article an extension of the infomax algorithm is proposed that is able to separate mixed signals with any sub- or super-gaussian distributions. this ability is the results of using two different nonlinear functions and new coefficients in the learning rule. in this paper we show how we can use the distribution...

assigning facilities to locations is one of the important problems, which significantly is influence in transportation cost reduction. in this study, we solve quadratic assignment problem (qap), using a meta-heuristic algorithm with deterministic tasks and equality in facilities and location number. it should be noted that any facility must be assign to only one location. in this paper, first o...

this paper presents a new optimization algorithm to design an optimal proportional, integral, derivative (pid) controller in hydro-turbine generator governor for damping output frequency oscillations. in this research, we utilize a stochastic and optimal based pid controller to control frequency-response of the hydro turbine. the proposed algorithm is employed to design an optimal pid controlle...

this paper proposes the exchange market algorithm (ema) to solve the combined economic and emission dispatch (ceed) problems in thermal power plants. the ema is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. existence of two seeking operators in ema provides a high ability in exploiting global optimum point. in order to show the capabilities ...

the cuckoo search algorithm is a recently developedmeta-heuristic optimization algorithm, which is suitable forsolving optimization problems. to enhance the accuracy andconvergence rate of this algorithm, an improved cuckoo searchalgorithm is proposed in this paper. normally, the parametersof the cuckoo search are kept constant. this may lead todecreasing the efficiency of the algorithm. to cop...

in this study, a hybrid algorithm is presented to tackle multi-variables robust design problem. the proposed algorithm comprises neural networks (nns) and co-evolution genetic algorithm (cga) in which neural networks are as a function approximation tool used to estimate a map between process variables. furthermore, in order to make a robust optimization of response variables, co-evolution algor...

in descriptive statistics, there are two computational algorithms for determining the variance s2, of a set of observations : algorithm 1: s2= - , algorithm 2: s2= , where . it is interesting to discuss, which of the above formulas is numerically more trustworthy in machine numbers sets. i this paper, based on total effect of rounding error, we prove that the second algorithm is better than ...

the large amount of computation necessary for obtaining time optimal solution for moving a manipulator on specified path has made it impossible to introduce an on line time optimal control algorithm. most of this computational burden is due to calculation of switching points. in this paper a learning algorithm is proposed for finding the switching points. the method, which can be used for both ...

in this paper, we present a numerical algorithm for solving matrix equations $(a otimes b)x = f$  by extending the well-known gaussian elimination for $ax = b$. the proposed algorithm has a high computational efficiency. two numerical examples are provided to show the effectiveness of the proposed algorithm.