Sharif Digital Repository

Active Learning Method (ALM) is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as a macro-level brain imitation, has been inspired by some behavioral specifications of human brain and active learning ability. ALM is an adaptive recursive fuzzy learning algorithm, in which a complex Multi Input, Multi Output system can be represented as a fuzzy combination of several Single-Input, Single-Output systems. SISO systems as associative layer of algorithm capture partial spatial knowledge of sample data space, and enable a granular knowledge resolution tuning mechanism through the learning process. The... 

In systems with hysteresis behavior like magnetic cores, Piezo actuators, Shape Memory Alloy(SMA), we essentially need an accurate modeling of hysteresis either for design or performance evaluation; also in some control applications accurate system identification is needed. One of the famous methods of Hysteresis modeling is Preisach model. In this numerical method hysteresis is modeled by linear combination of smaller hysteresis loops as an elemental operator and local memory. In this paper we discuss those Radial Base artificial neural networks (RBF) which provides natural settings in accordance with the Preisach model. It is shown that the proposed approach can represent hysteresis... 

In this paper, an adaptive multi-model sliding mode controller for robotic manipulators is presented. By using the multiple models technique, the nominal part of the control signal is constructed according to the most appropriate model at different environments. Adaptive single-input-single-output (SISO) fuzzy systems or radial basis function (RBF) neural networks, regarding their functional equivalence property, are used to approximate the discontinuous part of control signal; control gain, in a classical sliding mode controller. The key feature of this scheme is that prior knowledge of the system uncertainties is not required to guarantee the stability. Also the chattering phenomenon in... 

In this study, the application of data-driven methods for performance prediction of a heat pump assisted humidification-dehumidification (HDH-HP) desalination system was investigated for the first time. Although HDH-HP desalination systems have been widely studied both theoretically and experimentally, the application of data-driven models as a powerful predictive tool has not yet been investigated in these systems. To fill this gap, three data-driven models (MLPANN, RBFANN, and ANFIS) were applied using 180 experimental samples. The gain output ratio (GOR), heat transfer rates of the evaporator Q̇e, and evaporative condenser Q̇c, were considered as outputs. The results indicate that the...