Predicting Short-Term Orders by an Improved Grey Neural Network Model

2013 ◽  
Vol 321-324 ◽  
pp. 2227-2231
Author(s):  
Sheng Long Yang ◽  
Sheng Ma Zhang ◽  
Cui Hua Wang ◽  
Jun Jie Ma
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Correction Method ◽  
Random Selection ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Network Parameter ◽  
Term Orders ◽  
Improved Pso

According to the validation that the random selection of the gray neural network parameters random selection is similar to initial the space position of the particle in the particle swarm algorithm, the gray neural network based on the modified particle swarm optimization (PSO) algorithm is established to improve the robustness and the precision of the net model with applying a improved PSO algorithm to instead of gradient correction method, updating the network parameter and searching the best individual in this algorithm. There are several methods to forecast the short-term orders, including BP, the gray network, the original PSO algorithm and the improved PSO algorithm. Comparing with these methods, the results demonstrated the grey network based on the improved PSO algorithm has better approximation ability and prediction accuracy.

Tuning the Structure and Parameters of a Neural Network by Using Cooperative Quantum Particle Swarm Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 1328-1332 ◽  
Author(s):  
Qi Feng Tang ◽  
Liang Zhao ◽  
Rong Bin Qi ◽  
Hui Cheng ◽  
Feng Qian
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Training Algorithms ◽  
Approximation Accuracy ◽  
Quantum Genetic Algorithm ◽  
Cooperation Mechanism ◽  
Swarm Algorithm ◽  
Redundant Structure

In this paper, a cooperative quantum genetic algorithm-particle swarm algorithm (CQGAPSO) is applied to tune both structure and parameters of a feedforward neural network (NN) simultaneously. In CQGAPSO algorithm, QGA is used to optimize the network structure and PSO algorithm is employed to search the parameters space. The amplitude-based coding method and cooperation mechanism improve the learning efficiency, approximation accuracy and generalization of NN. Furthermore, the ill effects of approximation ability caused by redundant structure of NN are eliminated by CQGAPSO. The experimental results show that the proposed method has better prediction accuracy and robustness in forecasting the sunspot numbers problems than other training algorithms in the literatures.

A New Particle Swarm Algorithm for Solving Constrained Optimization Problems

2013 ◽  
Vol 734-737 ◽  
pp. 2875-2879
Author(s):  
Tie Bin Wu ◽  
Yun Cheng ◽  
Yun Lian Liu ◽  
Tao Yun Zhou ◽  
Xin Jun Li
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Constrained Optimization Problems ◽  
Particle Population ◽  
Point Set ◽  
Good Point Set ◽  
Improved Pso

Considering that the particle swarm optimization (PSO) algorithm has a tendency to get stuck at the local solutions, an improved PSO algorithm is proposed in this paper to solve constrained optimization problems. In this algorithm, the initial particle population is generated using good point set method such that the initial particles are uniformly distributed in the optimization domain. Then, during the optimization process, the particle population is divided into two sub-populations including feasible sub-population and infeasible sub-population. Finally, different crossover operations and mutation operations are applied for updating the particles in each of the two sub-populations. The effectiveness of the improved PSO algorithm is demonstrated on three benchmark functions.

Multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

2021 ◽  
pp. 1-12
Author(s):  
Lan Zhang ◽  
Lei Xu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Short Term ◽  
Swarm Optimization ◽  
Load Forecast ◽  
Multi Objective ◽  
Recursive Neural Network

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

Combined Forecasting Model Based on BP Improved by Particle Swarm Optimization and its Application

2014 ◽  
Vol 644-650 ◽  
pp. 1954-1956
Author(s):  
Run Ya Li ◽  
Xiang Nan Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Power Load ◽  
Bp Neural Network Model

The BP neural network as the traditional prediction method has certain advantages, but it has some drawbacks, Such as slow convergence and sensitive to the initial weights, etc. The PSO algorithm is introduced into the neural network training, using the particle swarm algorithm to optimize the neural network weights and threshold. Through the establishment of the particle swarm - BP neural network model for power load budget, it improves the accuracy and stability of the forecast.

scholarly journals Study of BP neural network based on improved particle swarm algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032041
Author(s):  
Xiaoqian Ma ◽  
Liyuan Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Particle Swarm ◽  
Correction Method ◽  
Series Data ◽  
Particle Swarm Algorithm ◽  
The Neural Network ◽  
Swarm Algorithm

Abstract This paper uses first-order difference to transform non-smooth data into smooth time series data, determines the p and q parameters in the model by judging the trailing and truncated nature of ACF, PACF, and finally establishes the ARIMA model after ACI, BCI detection. According to the parameters of the neural network randomly selected similar to the initial spatial position of the particles in the particle swarm algorithm, the improved particle swarm algorithm is used instead of the gradient correction method to precisely adjust the parameters and establish the BP neural network, which improves the robustness and accuracy of the prediction model.

scholarly journals Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zahra Shafiei Chafi ◽  
Hossein Afrakhte
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electrical Load ◽  
Short Term ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Electrical Load Forecasting

Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

scholarly journals Short-time traffic flow prediction based on immune particle swarm neural networks

2021 ◽  
Author(s):  
Xiaomo Yu ◽  
Yuheng Kang ◽  
Zhou Shen
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Swarm Algorithm ◽  
Short Time

Abstract The concentration-based selection mechanism in the immune theory can avoid the shortcomings of the particle swarm algorithm in balancing population convergence and individual diversity, and enable the improved particle swarm algorithm to optimize the configuration of BP neural network parameters and improve the accuracy of short-term traffic flow prediction. The simulation experiments show that the immune particle swarm optimized BP neural network can effectively improve the prediction accuracy of short-term traffic flow and reduce the prediction error.

Integration Particle Swarm Algorithm and its Application in Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 2133-2136
Author(s):  
Jun Pan ◽  
Xu Cao
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Algorithm Optimization ◽  
Bp Network ◽  
Node Number ◽  
The Neural Network ◽  
Swarm Algorithm ◽  
Hidden Layer

This paper puts forward a kind of evolutionary algorithm and the neural network combining with the new method of optimization of hidden layer nodes number of particle swarm algorithm of neural network. The BP neural network technology is a kind of more mature neural network method, but there are easy to fall into local minimum value, unable to accurately determine the number of hidden layer nodes of the network, the disadvantages such as slow convergence speed. This paper puts forward the optimization with hidden node number of particle swarm neural network (HPSO neural network) is the hidden layer of BP network node number as a particle swarm optimization (PSO) algorithm is an important optimization goal, network of hidden layer nodes and the number of each BP network weights and closed value together, common as particle swarm algorithm optimization goal.

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