Preservation of local similarity structure is a key challenge in deep clustering. Many recent clustering methods therefore use autoencoders to help guide the model's neural network towards an embedding which more reflective input space geometry. However, work has shown that autoencoder-based models can suffer from objective function mismatch (OFM). In order improve preservation structure, while...

in this article, a multi-objective function for placement of distributed generation (dg) and capacitors with thetap setting of under load tap changer (ultc) transformer is introduced. most of the recent articles have paidless attention to dg, capacitor placement and ultc effects in the distribution network simultaneously. insimulations, a comparison between different modes was carried out with,...

the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

in this paper, an optimization problem with a linear objective function subject to a consistent finite system of fuzzy relation inequalities using the max-product composition is studied. since its feasible domain is non-convex, traditional linear programming methods cannot be applied to solve it. we study this problem and capture some special characteristics of its feasible domain and optimal s...

data envelopment analysis (dea) is a method to evaluate the relative efficiency of decision making units (dmus). in this method, the issue has always been to determine a set of weights for each dmu which often caused many problems. since the dea models also have the multi-objective linear programming (molp) problems nature, a rational relationship can be established between molp and dea problem...

The problem of separating a linear or nonlinear mixture of independent sources has been the focus of many studies in recent years. It is well known that the classical principal component analysis method, which is based on second order statistics, performs poorly even in the linear case, if the sources do not have Gaussian distributions. Based on this fact, several algorithms take in account hig...

We introduce a weight update formula that is expressed only in terms of firing rates and their derivatives and that results in changes consistent with those associated with spike-timing dependent plasticity (STDP) rules and biological observations, even though the explicit timing of spikes is not needed. The new rule changes a synaptic weight in proportion to the product of the presynaptic firi...