Abstract: In this paper, a TSK-type Neuro-fuzzy Inference
System that combines the features of fuzzy sets and neural networks
has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled
Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate
the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on
Particle Swarm Optimization algorithm (PSO) and Genetic
Algorithm (GA).
Abstract: The main objective of this paper is applying a
comparison between the Wolf Pack Search (WPS) as a newly
introduced intelligent algorithm with several other known algorithms
including Particle Swarm Optimization (PSO), Shuffled Frog
Leaping (SFL), Binary and Continues Genetic algorithms. All
algorithms are applied on two benchmark cost functions. The aim is
to identify the best algorithm in terms of more speed and accuracy in
finding the solution, where speed is measured in terms of function
evaluations. The simulation results show that the SFL algorithm with
less function evaluations becomes first if the simulation time is
important, while if accuracy is the significant issue, WPS and PSO
would have a better performance.
Abstract: In this paper, a new efficient method for load balancing in low voltage distribution systems is presented. The proposed method introduces an improved Leap-frog method for optimization. The proposed objective function includes the difference between three phase currents, as well as two other terms to provide the integer property of the variables; where the latter are the status of the connection of loads to different phases. Afterwards, a new algorithm is supplemented to undertake the integer values for the load connection status. Finally, the method is applied to different parts of Tabriz low voltage network, where the results have shown the good performance of the proposed method.
Abstract: Due to short product life cycles, increasing variety of
products and short cycles of leap innovations manufacturing
companies have to increase the flexibility of factory structures.
Flexibility of factory structures is based on defined factory planning
processes in which product, process and resource data of various
partial domains have to be considered. Thus factory planning
processes can be characterized as iterative, interdisciplinary and
participative processes [1]. To support interdisciplinary and
participative character of planning processes, a federative factory
data management (FFDM) as a holistic solution will be described.
FFDM is already implemented in form of a prototype. The interim
results of the development of FFDM will be shown in this paper. The
principles are the extracting of product, process and resource data
from documents of various partial domains providing as web services
on a server. The described data can be requested by the factory
planner by using a FFDM-browser.