scholarly journals Identification of Shaft Centerline Orbit for Wind Power Units Based on Hopfield Neural Network Improved by Simulated Annealing

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Kun Ren ◽  
Jihong Qu
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Wind Power ◽  
Hopfield Neural Network ◽  
Affine Invariant ◽  
Characteristic Parameters ◽  
Invariant Moments ◽  
The Status ◽  
Identification Model ◽  
Better Than

In the maintenance system of wind power units, shaft centerline orbit is an important feature to diagnosis the status of the unit. This paper presents the diagnosis of the orbit as follows: acquire characters of orbit by the affine invariant moments, take this as the characteristic parameters of neural networks to construct the identification model, utilize Simulated Annealing (SA) Algorithm to optimize the weights matrix of Hopfield neural network, and then some typical faults were selected as examples to identify. Experiment’s results show that SA-Hopfield identification model performed better than the previous methods.

scholarly journals Research on Material Allocation Path Based on Hopfield Neural Network and Simulated Annealing Hybrid Algorithm

2020 ◽  
Vol 1682 ◽  
pp. 012003
Author(s):  
Junpeng Xi ◽  
Xudong Zhao ◽  
Yingshuang Zhu ◽  
Baoqiang Xiao ◽  
Shun Chen
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Hybrid Algorithm ◽  
Hopfield Neural Network

scholarly journals Interactive Entertainment Image Restoration Method Based on Hopfield Neural Network

2018 ◽  
Vol 232 ◽  
pp. 01008
Author(s):  
Shuangqing lv
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Hopfield Neural Network ◽  
Original Data ◽  
Small Scale ◽  
Interactive Entertainment ◽  
Degraded Image ◽  
Restoration Method ◽  
Traditional Image ◽  
Better Than

The traditional image restoration methods of interactive entertainment are based on the original data. This paper proposes an interactive entertainment image restoration method based on Hopfield neural network. Firstly, the nonlinear mapping relationship between the degraded image and the real image is preliminarily established through the network, and then optimized by the algorithm. Finally, the image restoration can be achieved through the network. The experiments show that it has higher feasibility and the recovery effect on small-scale blur is better than the existing method.

Study on Detection Method for Crack in Eggs Based on Computer Vision and Support Vector Machine Neural Network

2014 ◽  
Vol 472 ◽  
pp. 176-179 ◽  
Author(s):  
Jian Yang ◽  
Ying Shi ◽  
Wei Zhou ◽  
Yong Shun Che
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Traditional Method ◽  
Detection Method ◽  
Support Vector ◽  
Characteristic Parameters ◽  
Sample Data ◽  
Better Than

To improve the accuracy of detection and classification of egg with cracks, this paper is to add Support Vector Machine to neural network to automatically identify and classify the eggs with cracks. Firstly process the egg images with light-transmitting were obtained by the computer vision device including denoising, threshold segmentation. Five characteristic parameters of crack areas and noise areas were acquired. Secondly train SVM Neural Network and identify the eggs with cracks by five parameters data as the sample data. The correct discerning rate of grading table eggs is 98.07%. It proves better than traditional method in terms of prediction accuracy and robustness. The generalization ability of SVM Neural Network is strengthened.

Feature Extraction and Recognition Based on the Biological Analysis

2014 ◽  
Vol 513-517 ◽  
pp. 3180-3183
Author(s):  
Wen Cang Zhao ◽  
Fan Wang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Characteristic Parameters ◽  
Invariant Moments ◽  
Biological Analysis ◽  
Stored Product Pests ◽  
Abdominal Perimeter ◽  
Stable Characteristic

In this paper the extracted features including rectangularity,elongation, invariant moments and the four ratios of the stored product pests, which are the ratio of antennae area to torso area, the ratio of antennae perimeter to torso perimeter,the ratio of head and chest area to abdominal area, the ratio of head and chest perimeter to abdominal perimeter. Then these 13 characteristic parameters are input to BP neural network and SVM for recognition and classification. Form the results we can see that the 13 features in this paper can be well reflected the stable characteristic information of the stored product pests.

scholarly journals A Self-Organizing Neural Network for the Traveling Salesman Problem That Is Competitive with Simulated Annealing

1996 ◽  
Vol 8 (2) ◽  
pp. 416-424 ◽  
Author(s):  
Marco Budinich
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Unsupervised Learning ◽  
Traveling Salesman Problem ◽  
Approximate Solutions ◽  
Elastic Net ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem ◽  
Better Than ◽  
Self Organizing

Unsupervised learning applied to an unstructured neural network can give approximate solutions to the traveling salesman problem. For 50 cities in the plane this algorithm performs like the elastic net of Durbin and Willshaw (1987) and it improves when increasing the number of cities to get better than simulated annealing for problems with more than 500 cities. In all the tests this algorithm requires a fraction of the time taken by simulated annealing.

RBF Neural Network Wind Power Prediction Based on Chaos Theory

2014 ◽  
Vol 1070-1072 ◽  
pp. 315-318
Author(s):  
Li Dong Zhang ◽  
Shan Shan Li ◽  
Xu Dong He
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Lyapunov Exponent ◽  
Wind Power ◽  
Chaos Theory ◽  
Rbf Neural Network ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Better Than

Using the C - C method to reconstruct the phase space of wind power time series, get the maximum wind power time series Lyapunov exponent, confirmed that the wind power time series have chaotic characteristics. Followed by the radial basis function (RBF) neural network model for wind power chaotic local multi-step prediction, results show that the prediction effect is better than that of the predicted effect of 48 hours for 24 hours.

Hopfield Neural Network Modeling Method Based on Subset KPCA with FCMC Clustering

2014 ◽  
Vol 1049-1050 ◽  
pp. 1658-1661
Author(s):  
Xiang Jie Luo ◽  
Xiao Yong Li ◽  
Hong Qu ◽  
Yue Bo Meng
Keyword(s):  
Neural Network ◽  
Nonlinear Modeling ◽  
Network Modeling ◽  
Hopfield Neural Network ◽  
Modeling Method ◽  
Neural Network Modeling ◽  
Fuzzy C Means Clustering ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
Better Than

In order to improve the performance of nonlinear modeling, a Hopfield neural network modeling method based on Subset Kernel Principal Components Analysis (SubKPCA) with Fuzzy C-Means Clustering (FCMC) is proposed. The simulation result shows that, the performance of the proposed method is better than that of Hopfield neural network based on KPCA. It also is effective and feasible to establish the model for the estimation of missing flight data.

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