Dynamic Fuzzy Logic Control of GeneticAlgorithm Probabilities

Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other methods have parameters(population size, probabilities of crossover and mutation) which need to be tune orchosen.In this paper, our project is based on an existing hybrid genetic algorithmworking on the multiprocessor scheduling problem. We propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem.The algorithm consists in adding a fuzzy logic controller to control and tunedynamically different parameters (probabilities of crossover and mutation), in anattempt to improve the algorithm performance. For this purpose, we will design afuzzy logic controller based on fuzzy rules to control the probabilities of crossoverand mutation. Compared with the Standard Genetic Algorithm (SGA), the resultsclearly demonstrate that the FLGA method performs significantly better.

DC Motor Speed Control via Fuzzy / Pole Placement / PI Controller

This report presents a new way of control engineering. Dc motor speed controlled by three controllers PID, pole placement and Fuzzy controller and discusses the advantages and disadvantages of each controller for different conditions under loaded and unloaded scenarios using software Matlab. The brushless series wound Dc motor is very popular in industrial application and control systems because of the high torque density, high efficiency and small size. First suitable equations are developed for DC motor. PID controller is developed and tuned in order to get faster step response. The simulation results of PID controller provide very good results and the controller is further tuned in order to decrease its overshoot error which is common in PID controllers. Further it is purposed that in industrial environment these controllers are better than others controllers as PID controllers are easy to tuned and cheap. Pole placement controller is the best example of control engineering. An addition of integrator reduced the noise disturbances in pole placement controller and this makes it a good choice for industrial applications. The fuzzy controller is introduce with a DC chopper to make the DC motor speed control smooth and almost no steady state error is observed. Another advantage is achieved in fuzzy controller that the simulations of three different controllers are compared and concluded from the results that Fuzzy controller outperforms to PID controller in terms of steady state error and smooth step response. While Pole placement controller have no comparison in terms of controls because designer can change the step response according to nature of control systems, so this controller provide wide range of control over a system. Poles location change the step response in a sense that if poles are near to origin then step response of motor is fast. Finally a GUI of these three controllers are developed which allow the user to select any controller and change its parameters according to the situation.

Genetic Control of Residual Variance for Teat Number in Pigs

The genetic improvement in litter size in pigs has been substantial during the last 10-15 years. The number of teats on the sow must increase as well to meet the needs of the piglets, because each piglet needs access to its own teat. We applied a genetic heterogeneity model on teat numberin sows, and estimated medium-high heritability for teat number (0.5), but low heritability for residual variance (0.05), indicating that selection for reduced variance might have very limited effect. A numerically positive correlation (0.8) between additive genetic breeding values for mean and for variance was found, but because of the low heritability for residual variance, the variance will increase very slowly with the mean.

Internal Control application : Empirical evidence from Sweden

Aim The aim of this study is to explore based on internationally recognised frameworks: 1. how internal control structures are applied in Sweden among different sectors; 2. how organizational size and environment affect internal control structures; and 3. the impact of internal control structures on organizational performance. Methods A quantitative method was used in the data collection and analysis. The sample consisted of 1117 organizations operating in Sweden. A mean analysis was conducted to measure the level of internal control structures among different industries, organizational sizes, and different choices of listing in the stock exchange market. Person’s correlation analysis was then used to explore possible correlations between external environmental factors and internal control structures, and internal control structures and organizational performance. Lastly, a structural model was built to measure the impact of internal control structures on organizational performance. The measurements of internal control structures and organizational performance are based on COSO framework’s principles and objectives. Results This study gives an insight on how internal control structures are applied across industrial sectors in Sweden, with financial institutions and manufacturing organizations having notably higher levels of internal control structures. Additionally, it provides evidence of the impact external environmental factors have on internal control structures. Furthermore, it shows that organizations that are listed in the Swedish stock exchange market have an equivalent level of internal control structures to those registered in the American stock exchange market. In contrast, organisations that are not listed in the stock exchange market have a notably lower level of internal control structures. Lastly, it illustrates the positive impact the presence of internal control structures has on organizational performance. 3 | P a g e Conclusion The results highlight a crucial role the supervisory authority Finansinspektionen (FI) has in regulating the Swedish financial market. They also show that the stability of the Swedish business environment has had a positive impact on the level of internal control structures.