Improved Dynamic Process Neural Network and its Application

2010 ◽  
Vol 458 ◽  
pp. 143-148
Author(s):  
Pei Yin Zhang ◽  
G.B. Yu ◽  
B. Dai ◽  
Ying Jie Ao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Dynamic Process ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Test Results ◽  
Tourism Demand ◽  
Swarm Optimization ◽  
Proposed Model

The tourism demand is essential in terms of national economy and the improvement of people’ income. But it is difficult for traditional methods to predict the tendency of the tourism demand. In this paper, a time series prediction method based on dynamic process neural network (DPNN) is proposed to solve this problem. An improved particle swarm optimization (IPSO) is developed. By tuning the structure and improving the connection weights of PNN simultaneously, a partially connected DPNN can be obtained. The effectiveness of the proposed DPNN is proved by Henon system. Finally, the proposed DPNN is utilized to predict the tourism demand, and the test results indicate that the proposed model seems to perform well and appears suitable for using as a predictive maintenance tool.

Study on Chaotic Time Series Prediction Algorithm for Kent Mapping Based on Particle Swarm Optimization

2014 ◽  
Vol 511-512 ◽  
pp. 941-944 ◽  
Author(s):  
Hong Li Bian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Swarm Optimization ◽  
Chaotic Time Series Prediction

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

scholarly journals A Network Traffic Prediction Model Based on Quantum-Behaved Particle Swarm Optimization Algorithm and Fuzzy Wavelet Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Kun Zhang ◽  
Zhao Hu ◽  
Xiao-Ting Gan ◽  
Jian-Bo Fang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Traffic ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Swarm Optimization ◽  
Fuzzy Wavelet Neural Network

Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO) was introduced. Then, the structure and operation algorithms of WFNN are presented. The parameters of fuzzy wavelet neural network were optimized by QPSO algorithm. Finally, the QPSO-FWNN could be used in prediction of network traffic simulation successfully and evaluate the performance of different prediction models such as BP neural network, RBF neural network, fuzzy neural network, and FWNN-GA neural network. Simulation results show that QPSO-FWNN has a better precision and stability in calculation. At the same time, the QPSO-FWNN also has better generalization ability, and it has a broad prospect on application.

Chaotic Time Series Prediction for Tent Mapping Based on BP Neural Network Optimized Glowworm Swarm Optimization

2014 ◽  
Vol 513-517 ◽  
pp. 1096-1100
Author(s):  
Yue Hou ◽  
Hai Yan Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Connection Weight ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization

In order to improve the neural network structure and setting method of parameters, based on the glowworm swarm optimization (GSO) and BP neural network (BPNN), an algorithm of BP neural network optimized glowworm swarm optimization (GSOBPNN) is proposed. In the algorithm, GSO is used to obtain better network initial threshold and weight so as to compensate the defect of connection weight and thresholds choosing of BPNN, thus BPNN can have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series of tent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series.

Chaotic Time Series Prediction Algorithm for Lori Mapping Based on Optimized BP Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 2108-2111
Author(s):  
Hong Li Bian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Swarm Optimization ◽  
Chaotic Time Series Prediction

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Lori mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

scholarly journals Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5609 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Deep Learning Neural Network

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

GRU-corr Neural Network Optimized by Improved PSO Algorithm for Time Series Prediction

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040010
Author(s):  
Shao-Pei Ji ◽  
Yu-Long Meng ◽  
Liang Yan ◽  
Gui-Shan Dong ◽  
Dong Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Series Data ◽  
Multi Scale ◽  
Network Parameters ◽  
Angle Data

Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters and obtain the high-precision global optimization of network parameters, weight parameters and the hidden nodes number of GRU-corr is optimized by ASPSO, and a time series prediction model (ASPSO-GRU-corr) is proposed based on the GRU-corr optimized by ASPSO. In the experiment, a comparative analysis of the optimization performance of ASPSO on a benchmark function was performed to verify its validity, and then the ASPSO-GRU-corr model is used to predict the ship motion cross-sway angle data. The results show that, ASPSO has better optimization performance and convergence speed compared with other algorithms, while the ASPSO-GRU-corr has higher generalization performance and lower architecture complexity. The ASPSO-GRU-corr can reveal the intrinsic multi-scale composite features of the time series, which is a reliable nonlinear and non-steady time series prediction method.

Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network

2020 ◽  
Vol 52 (2) ◽  
pp. 1485-1500
Author(s):  
Jiaojiao Hu ◽  
Xiaofeng Wang ◽  
Ying Zhang ◽  
Depeng Zhang ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Prediction ◽  
Prediction Method
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