"Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification

In this study, the premise and consequent parameters of ANFIS are optimized using Genetic Algorithm (GA) based on a population algorithm. The proposed approach is applied to the nonlinear dynamic system identification problem. The simulation results of the method are compared with the Backpropagation (BP) algorithm and the results of other methods that are available in the literature. With this study it was observed that the optimisation of ANFIS parameters using GA is more successful than the other method

Power fluctuation control on a DC-residential network using Tanaka's Optimization and Tabu Search Approach

The power fluctuation problem of renewable energy sources, frequency and voltage deviations are usually occurred in the isolated power systems, in which the ability to maintain stable supply-demand balance is low. Smart grid system is a solution to this problem and because of that the idea of smart grid concept is proposed which cooperatively could balance the supply-demand between power supply side and power demand side. The installation of photovoltaic (PV) system is proposed in a residential building which can be straight forwardly connected to DC sources. DC systems are required to bring lower costs by elimination of inverter and rectifier circuits and it may be possible to operate the PV system with high efficiency. Therefore, this study presents a DC smart grid system for a small residential network that sourced by solar energy which consist of a PV generator, a solar collector (SC), a heat pump (HP) and a battery. Battery and heat pump are used as controllable loads. Then, in order to minimize the interconnection, point power flow fluctuations and its operational cost, Tanaka’s Optimization and Tabu Search Approach is employed. Tanaka’s Optimization is used to obtain the optimal operation of thermal unit and controllable loads. Meanwhile, Tabu Search approach helps to control the power consumption of controllable load and discharge and/or charge output of the battery. From the results it has been found that the interconnection point power flow in the smart house could be controlled within the given bandwidth from the power reference. By smoothing the interconnection point of power flow, the electricity cost could be reduced due to reduction of contract fee with the electricity power company. Consequently, we can expect high quality power supply, higher efficiency of power transfer and lower CO₂ emissions

Training recurrent neural networks by using parallel tabu search algorithm based on crossover operation

There are several heuristic optimisation techniques used for numeric optimisation problems such as genetic algorithms, neural networks, simulated annealing, ant colony and tabu search algorithms. Tabu search is a quite promising search technique for non-linear numeric problems, especially for the problems where an optimal solution must be determined on-line. However, the converging speed of the basic tabu search to the global optimum is the initial solution dependent since it is a form of iterative search. In order to overcome this drawback of basic tabu search, this paper proposes a new parallel model for the tabu search based on the crossover operator of genetic algorithms. After the performance of the proposed model was evaluated for the well-known numeric test problems, it is applied to training recurrent neural networks to identify linear and non-linear dynamic plants and the results are discussed. (C) 2004 Elsevier Ltd. All rights reserved