A Combination of Fuzzy Theory and Genetic-Neural Network Algorithm

2010 ◽  
Author(s):  
Tang Xiaoyi ◽  
Guo Qingping ◽  
Wu Peng ◽  
Song Huijuan
Keyword(s):  
Neural Network ◽  
Fuzzy Theory ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Genetic Neural Network

Nuclear Magnetic Image Segmentation of Rectal Cancer Based on Improved Genetic Neural Network

2020 ◽  
Vol 10 (7) ◽  
pp. 1644-1653
Author(s):  
Danyang Li ◽  
Yumei Sun ◽  
Wanqing Liu ◽  
Bing Hu ◽  
Jianlin Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Rectal Cancer ◽  
Image Segmentation ◽  
Fitness Function ◽  
Segmentation Method ◽  
Magnetic Image ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Genetic Neural Network ◽  
Nuclear Magnetic

Image segmentation is the basis of image analysis and understanding, and has an unshakable position in the field of computer vision. In order to improve the accuracy of nuclear magnetic image segmentation of rectal cancer, this paper proposes an improved genetic neural network algorithm for the problems of traditional BP neural network algorithm. In order to enhance the network performance, this paper improves the genetic neural network from the two aspects of fitness function and genetic operator, which makes the training speed and convergence precision greatly improved. Target samples were analyzed by image histogram analysis, and the improved genetic neural network was used to learn the samples to obtain the training network. Taking the pixel matrix of the image as the input vector, it is put into the trained network for classification, and finally the segmentation is realized. The simulation experiment proves that compared with the classical image segmentation method, the improved genetic neural network image segmentation method has a good segmentation effect and is a feasible image segmentation method.

Application of Materials Design Based on Genetic Neural Network

2007 ◽  
Vol 280-283 ◽  
pp. 1837-1840
Author(s):  
Yilai Zhang ◽  
Xue Jian Li ◽  
Ling Ke Zeng ◽  
Cheng Kang Chang
Keyword(s):  
Neural Network ◽  
Materials Design ◽  
Aluminum Titanate ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Genetic Neural Network ◽  
Design Materials

Neural network (NN) is an effective method in the filed of materials design, but the convergent speed is decided by initial weights. This paper proposes genetic neural network algorithm (GNNA) to design materials. Aluminum titanate modification is studied by the method of GNNA. The results indicate the algorithm works well.

The Application of Neural Network Technology in the Fundus Angiography Image Segmentation

2014 ◽  
Vol 556-562 ◽  
pp. 5984-5988
Author(s):  
Dong Cui ◽  
Min Min Liu ◽  
Qing Jiao ◽  
Lei Hu ◽  
Di Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Vascular System ◽  
Measurement Data ◽  
Fluorescence Angiography ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Genetic Neural Network ◽  
Physical Changes ◽  
Important Basis ◽  
The Brain

The digital images as well as the data obtained by the fundus fluorescence angiography (FFA) can reflect the structure of retinal vessels, the hemodynamic changes, the vascular pathological physical changes and the pathological changes of other related structures, which have been widely applied in the differential diagnosis of the retina, the choroid and the optic nerve disease. According to the characteristics of FFA images, the BP neural network algorithm and the genetic neural network algorithm have been respectively employed to segment and contrast the lesion areas in fundus angiography vascular images as well as the fundus angiography images. Then the clinicians can get the more accurate measurement data of lesion areas and observe the more subtle vascular changes, which can provide an important basis for the treatment of the heart, the brain vascular system and the diabetes.

scholarly journals Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA-MLP-NN)

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Cong Yan
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Practical Significance ◽  
Multilayer Perceptrons ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Genetic Neural Network

Traditional symphony performances need to obtain a large amount of data in terms of effect evaluation to ensure the authenticity and stability of the data. In the process of processing the audience evaluation data, there are problems such as large calculation dimensions and low data relevance. Based on this, this article studies the audience evaluation model of teaching quality based on the multilayer perceptron genetic neural network algorithm for the data processing link in the evaluation of the symphony performance effect. Multilayer perceptrons are combined to collect data on the audience’s evaluation information; genetic neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all vocal data of the symphony performance process and effects according to different characteristics and expressions of the audience evaluation. Changes are analyzed and evaluated accurately. The experimental results show that the performance evaluation model of symphony performance based on the multilayer perceptron genetic neural network algorithm can be quantitatively evaluated in real time and is at least higher in accuracy than the results obtained by the mainstream evaluation method of data postprocessing with optimized iterative algorithms as the core 23.1%, its scope of application is also wider, and it has important practical significance in real-time quantitative evaluation of the effect of symphony performance.

scholarly journals Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ze Fu ◽  
Bo Zhang ◽  
Lingjun Ou ◽  
Kaiyang Sun ◽  
Xinyi Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Demographic Information ◽  
Intelligent Algorithm ◽  
Customer Data ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Customer Classification ◽  
Genetic Neural Network

Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.

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