###### Abstract  
The shunt active power filter (SAPF) is a widely used tool for compensation of disturbances in three-phase electric power systems. A high number of control methods have been successfully developed, including strategies based on artificial neural networks. However, the typical feedforward neural network, the multilayer perceptron, which has provided effective solutions to many nonlinear problems, has not yet been employed with satisfactory performance in the implementation of the SAPF control for obtaining the reference currents. In order to prove the capabilities of this simple neural topology, this work describes a suitable strategy of use, based on the accurate estimation of the Fourier coefficients corresponding to the fundamental harmonic of any distorted voltage or current  
as hysteresis band control (HB) [2]. Other important issues are the control of the power converter DC side [3] and the phase synchronization techniques [4]. The purpose of some APF control methods is limited to harmonic distortion mitigation, whereas many others try to achieve both harmonic suppression and reactive power compensation and may include addressing the load currents unbalance. The use of artificial neural networks (ANN) in the field of power engineering is well known. They make use of parallel computing, allowing the design of control circuits of high speed and reliability. In fact, ANNs have been systematically applied in the development of APF-based equipment, even though there are countless examples of non-ANN control techniques, as in [5; 6; 7]. A type of ANN usually applied to the APF control is the Adaline, based on the adaptive algorithm of Widrow-Hoff. Thus, in [8; 9], the Adaline network is used for extraction of the reference currents and the multilayer perceptron network (MLP) replaces the HB control. Moreover, in [10; 11; 12; 13], the core of the APF control is an Adaline network, which works with an on-line algorithm, under different load conditions. The work in [14] shows a comparative review of several control methods, including the study of the Adaline, highlighting some of its advantages. However, in these adaptive cases, the appropriate choice of the learning parameter for each problem is very important in order to avoid convergence problems; this is one of its weaknesses. However, recent works [15; 16; 17] illustrate that researchers continue to propose advances in the application of the Adaline. Other ANN types have also been applied to the APF, but with less relevance. This is the case of the radial basis function networks (RBF) and different topologies of recurrent neural networks (RNN). The RBF has been applied in [18] for extracting harmonic amplitudes, in [19] to support a complex adaptive sliding mode control (SMC), and in [20], it is used in combination with the P-Q power theory to obtain reference signals. Regarding the RNN, which has a structure close to the MLP, but with internal feedback connections, in [21; 22], it supports complex types of sliding mode controls in the APF. The authors of [23] achieved good results using the so-called NARX recurrent network for extracting the reference signals. Additionally, the RNN called echo-state network (ESN) has gained importance in recent years for extracting fundamental current components [24; 25]. The feedforward or MLP networks have shown to be useful in replacing the HB controller to generate the IGBTs trigger signals [8; 9; 17; 26; 27]. Some authors have applied the MLP [28; 29; 30] to replace the extended PI control [31] of the DC side capacitor voltage, _Vdc_, of power converters. To a lesser extent, the MLP networks have also been used to obtain the reference signals [32; 33], with variable results. MLP networks seemed to be a promising tool for harmonic detection and mitigation many years ago [32; 34; 35; 36]. Nevertheless, they could not be successfully applied for the generation of the reference currents when trying to achieve accurate APF control for all kinds of distortions.