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Evolutionary algorithms combined with artificial neural network (ANN) have been applied in RFID tag antenna optimization platform. An effective method for RFID tag antenna optimization by particle swarm optimization (PSO) algorithm or genetic algorithm (GA) combined with ANN is presented in this paper. ANN is used to establish the non-linear model of tag antenna which is shown to be as accurate as...
This paper introduces a hybrid evolutionary optimization algorithm as a tool for training an Artificial Neural Network used for production forecasting of solar energy PV plants. This hybrid technique is developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This...
The dependable operation of brain-computer interfaces (BCI) based on electroencephalogram (EEG) signals requires precise classification of multi-channel EEG signals. The design of EEG interpretation and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements...
This paper reviews new approaches in modern research using Artificial Intelligent (AI) techniques to develop power system stabilizer (PSS). These techniques are Artificial Neural Network (ANN), fuzzy logic, hybrid artificial intelligent, expert systems, and optimization techniques base AI such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS) algorithm, etc. Research showed...
In this article, swarm intelligence approach is proposed for the solution of problems involved in differential equations of first order. The modeling of these problems is performed by artificial neural network that have universal approximation capabilities. A new particle swarm optimization algorithm is used to optimize the adaptive weights of neural network. The proposed method is successfully applied...
Despite a variety of artificial neural network (ANN) categories, backpropagation network (BP) and Elman recurrent network (ERN) are the widespread modus operandi in real applications. However, there are many drawbacks in BP network, for instance, confinement in finding local minimum and may get stuck at regions of a search space or trap in local minima. To solve these problems, various optimization...
An artificial neural network (ANN) is proposed to predict the input impedance of a broadband antenna as a function of its geometric parameters. The input resistance of the antenna is first parameterized by a Gaussian model, and the ANN is constructed to approximate the nonlinear relationship between the antenna geometry and the model parameters. Introducing the model simplifies the ANN and decreases...
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