The prediction of wind farm output power is considered as an effective way to increase the wind power capacity and improve the safety and economy of power system. It is one of the hot research topics on wind power. The wind farm output power is related to many factors such as wind speed, temperature, etc., which is difficult to be described by some mathematical expression. In this paper, Back P...

The paper proposes an approach to optimize the connection weights and network structure of BP neural network (BPNN) which based on improved particle swarm optimization (PSO) algorithm. For each network structure, the algorithm generates a series of particles which consist of connection weights and threshold values, and selects the best network structure according to the improved PSO algorithm. ...

In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electrocardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complica...

neural network algorithms have been applied to a variety of areas of engineering and microwave structures. Neural networks are also able to model nonlinear relations between different data sets. Owing to this feature, an introduced neural network model (INN) based on particle swarm optimization (PSO) training algorithm (INN-PSO) is presented for pseudomorphic high electron mobility transistor (...

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors a...

Considering the fact that BP Neural Network has the defects of being easily falling into partial extreme value, to avoid the limitation of particle swarm algorithm we come up with an improved BP Neural Network applied in Neural Network training. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO first, and then using the developed PSO ways to optimi...

Recently, Particle Swarm Optimization (PSO) has evolved as a promising alternative to the standard backpropagation (BP) algorithm for training Artificial Neural Networks (ANNs). PSO is advantageous due to its high search power, fast convergence rate and capability of providing global optimal solution. In this paper, the authors explore the improvements in forecasting accuracies of feedforward a...

Developing artificial neural network (ANN), a model to make a correct prediction of required force and torque in ring rolling process is developed for the first time. Moreover, an optimal state of process for specific range of input parameters is obtained using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. Radii of main roll and mandrel, rotational speed of main roll, pr...