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.

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.

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.

A Quantum-Behaved Particle Swarm Algorithm Combined with Chaotic Mutation

2012 ◽  
Vol 605-607 ◽  
pp. 2442-2446
Author(s):  
Xin Ran Li ◽  
Yan Xia Jin
Keyword(s):  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Convergence Problem ◽  
Early Maturity ◽  
Classical Function ◽  
Function Test ◽  
Quantum Behavior ◽  
Swarm Algorithm

The article puts forward an improved PSO algorithm based on the quantum behavior——CMQPSO algorithm to improve premature convergence problem in particle swarm algorithm. The new algorithm first adopts Tent mapping initialization of particle swarm, searches each particle chaos, and strengthens the diversity of searching. Secondly, a method of effective judgment of early stagnation is embedded in the algorithm. Once the early maturity is retrieved, the algorithm mutates particles to jump out of the local optimum particle according to the structure mutation so as to reduce invalid iteration. The calculation of classical function test shows that the improved algorithm is superior to classical PSO algorithm and quantum-behaved PSO algorithm.

A Modified PSO-BP Algorithm in Hydraulic System Fault Diagnosis Application

2012 ◽  
Vol 182-183 ◽  
pp. 1145-1148 ◽  
Author(s):  
Zeng Shou Dong ◽  
Xiao Yu Zhang ◽  
Jian Chao Zeng
Keyword(s):  
Fault Diagnosis ◽  
Convergence Rate ◽  
Hydraulic System ◽  
Particle Swarm ◽  
Bp Algorithm ◽  
Particle Swarm Algorithm ◽  
Bp Network ◽  
Learning Factor ◽  
Swarm Algorithm ◽  
Modified Pso

BP neural network for failure pattern recognition has been used in hydraulic system fault diagnosis.However, its convergence rate is relatively small and always trapped at the local minima. So a new modified PSO-BP hydraulic system fault diagnosis method was proposed,which combined the respective advantages of particle swarm algorithm and BP algorithm. Firstly, the inertia weight and learning factor of the standard particle swarm algorithm was improved, then BP neural network’s weights and thresholds were optimized by modified PSO algorithm. BP network performance was ameliorated. The simulation results showed that this method improved the convergence rate of the BP network, and it could reduce the diagnostic errors.

scholarly journals Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

2018 ◽  
Vol 10 (12) ◽  
pp. 4445 ◽  
Author(s):  
Lejun Ma ◽  
Huan Wang ◽  
Baohong Lu ◽  
Changjun Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Operation ◽  
Computational Time ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm ◽  
Low Efficiency

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

Fault Diagnosis of Engine Ignition System Based on the Optimized BP Neural Networks by Improved Particle Swarm Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 366-370
Author(s):  
Zhi Mei Duan ◽  
Xiao Jin Yuan ◽  
Yan Jie Zhou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Particle Swarm Algorithm ◽  
Classification Result ◽  
Bp Network ◽  
Ignition System ◽  
Good Classification

In order to improve the accuracy of fault diagnosis of engine ignition system, in this paper, adaptive mutation particle swarm optimization (AMPSO) algorithm is used to optimize the weight of BP neural network. According to the fault feature of engine ignition system, the fault diagnosis is accomplished by the optimized BP neural network. The algorithm overcomes disadvantages that slowly convergence and easy to fall into local minima of standard PSO and BP network. The simulation results show that the method gains good classification result and has a certain practicality.

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