Vol 51, No 5 (2017)

This paper considers the problem of transforametion of Boolean functions into canonical polarized polynomials (Reed–Muller polynomials). Two Shannon functions are introduced to estimate the complexity of Boolean functions in the polynomials class under consideration. We propose three Boolean functions of n variables whose complexity (in terms of the number of terms) coincides with value. We investigate the properties of functions and propose their schematic realization on elements AND, XOR, and NAND.

This paper proposes a genetic algorithms (GA) optimization technique applied to power system stabilizer (PSS) for adapt a robust H₂ control based on linear quadratic controller (LQ) and Kalman Filter applied on automatic excitation control of powerful synchronous generators, to improve stability and robustness of power system type single machine connected to an infinite bus system (SMIB). Adaptation technique proposed of the robust H₂ control with the various electrical and mechanical parametric variations based on the optimization of the PSS parameters. The genetic algorithms is a search technique based on the mechanisms of natural selection of a genetic and evolution. This optimization technique is more used in the field of control for solve optimal choice problem of regulators parameters. The integration of GA to robust H₂ control with robustness test (electrical and mechanical parameters variations of the synchronous machine) show considerable improvements in dynamics performances, robustness stability and good adaptation of the robust H₂-PSS parameters under uncertain constraints. This present study was performed using our realized Graphical User Interface (GUI) developed under MATLAB.

It is expensive and time consuming to measure soil adsorption coefficient (logKoc) of compounds using traditional methods, and some existing models show lower accuracies. To solve these problems, a deep learning (DL) method based on undirected graph recursive neural network (UG-RNN) is proposed in this paper. Firstly, the structures of molecules are represented by directed acyclic graphs (DAG) using RNN model; after that when a number of such neural networks are bundled together, they form a multi-level and weight sharing deep neural network to extract the features of molecules; Third, logKoc values of compounds have been predicted using back-propagation neural network. The experimental results show that the UG-RNN model achieves a better prediction effect than some shallow models. After five-fold cross validation, the root mean square error (RMSE) value is 0.46, the average absolute error (AAE) value is 0.35, and the square correlation coefficient (R²) value is 0.86.

This paper investigates performance of two event-based controllers applied to an underactuated biped robot to stabilize its running gait in presence of uncertainties. Mechanism of the biped robot includes four links leg, one point mass at the hip, point feet, and three motors parallel to rotational springs. So it has one degree of underactuation during stance phase and three degrees of underactuation during flight phase. A discrete sliding mode controller (DSMC) in comparison with a discrete linear-quadratic regulator (DLQR) is examined in order to stabilize the fixed point of the corresponding Poincare map. Using numerical simulations, it is concluded that DSMC has a better performance regarding basin of attraction and convergence speed compared to DLQR, especially in presence of disturbances.

RNA computing is a new intelligent optimization algorithm, which combines computer science and molecular biology. Aiming at the weakness of slow convergence rate and poor global search ability in the basic ant colony optimization algorithm due to the unreasonable selection of parameters, this paper utilizes the combination of RNA computing and basic ant colony optimization algorithm to overcome the defects. An improved ant colony optimization algorithm based on RNA computing is proposed. In the iterative process of ant colony optimization algorithm, transformation operation, recombination operation and permutation operation in RNA computing are introduced to optimize the initial parameters including importance factor of pheromone trail α, importance factor of heuristic function β and pheromone evaporation rate ρ to improve the convergence efficiency and global search ability. The performance of the algorithm is evaluated on five instances of the library of traveling salesman problems (TSPLIB) and six typical test functions. The experimental results demonstrate that the proposed RNA-ant colony optimization algorithm is superior than basic ant colony optimization algorithm in optimization ability, reliability, convergence efficiency, stability and robustness.