scholarly journals Optimization and Simulation of Manuscript Management System Based on Fuzzy Genetic Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yongtai Sun
Keyword(s):  
Neural Network ◽  
Management System ◽  
Uml Modeling ◽  
Different Types ◽  
Optimization Simulation ◽  
Neural Network Algorithm ◽  
Genetic Neural Network ◽  
Optimization And Simulation ◽  
Fuzzy Genetic ◽  
E Mail

Manuscript management plays an important role in the whole periodical industry. Journals and magazine society receive different types of contribution documents from all over the world. Many submission files are mostly transmitted by e-mail, but there are some hidden disadvantages in e-mail. In view of this situation, this paper studies the establishment and optimization simulation of manuscript management system based on fuzzy genetic neural network (FGNN). On the basis of genetic neural network, combined with the advantages of FNN neural network algorithm, a FGNN structure is established to optimize the system, which is helpful to the learning and expression ability of the whole system. The results show that the FGNN can extract and express the structured data, and the submission management system runs faster. Then, UML modeling and PHP framework are used to realize the design and establishment of the system, and the simulation model of contribution system is constructed. The innovation of this study is to combine genetic neural network with FNN neural network, which can query, manage, and classify manuscripts quickly. It also improves the complex submission system and optimizes the submission process. The whole journal editing work has been effectively improved.

scholarly journals Development of Customer Relationship Management System in E-Commerce Based on BP Neural Network

2012 ◽  
Vol 6-7 ◽  
pp. 995-999
Author(s):  
Mei Ling Zhou ◽  
Jing Jing Hao
Keyword(s):  
Neural Network ◽  
Customer Relationship Management ◽  
Bp Neural Network ◽  
Management System ◽  
Customer Value ◽  
Relationship Management ◽  
Customer Relationship ◽  
Feed Forward Network ◽  
Neural Network Algorithm ◽  
Hidden Layer

BP neural network can learn and store a lot of input - output mode mapping, without prior reveal the mathematical equations describe the mapping. The model based on BP neural network algorithm is constituted by an input layer, output layer and one hidden layer, three-layer feed forward network. CRM is to acquire, maintain and increase the methods and processes of profitable customers. The core of CRM is the customer value management, customer value; it is divided into the de facto value, potential value and model value. The paper presents development of customer relationship management system in e-commerce based on BP neural network. The experiment shows BP is superior to RFCA in CRM.

scholarly journals Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2779
Author(s):  
Yaoming Zhuang ◽  
Chengdong Wu ◽  
Hao Wu ◽  
Zuyuan Zhang ◽  
Yuan Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Single Type ◽  
Different Types ◽  
Event Driven ◽  
Robot Networks ◽  
Neural Network Algorithm ◽  
Complex Monitoring ◽  
Maximum Entropy Function

Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.

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.

Implementation of Mail Classification Using Neural Networks of the Second Type Spline Weight Functions

2014 ◽  
Vol 513-517 ◽  
pp. 687-690 ◽  
Author(s):  
Dai Yuan Zhang ◽  
Lei Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recall Rate ◽  
High Accuracy ◽  
Weight Functions ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Sorting Process ◽  
E Mail

How to effectively filter out spam is a topic worthy of further study for the growing proliferation of spam. The main purpose of this paper is to apply a new neural network algorithm to the classification of spam. In this paper, we introduce a second type of spline weight function neural network algorithm, as well as e-mail feature extraction and vectorization, and then introduced the mail sorting process. Experiments show that it can get a relatively high accuracy and recall rate on the spam classification. Therefore, with this new algorithm, we can achieve better classification results.

Research on the Application of the Prediction of the Expressway Traffic Flow Based on the Neural Network with Genetic Algorithm

2011 ◽  
Vol 189-193 ◽  
pp. 4400-4404 ◽  
Author(s):  
Chun Mei Zhu ◽  
Chang Peng Yan ◽  
Xiao Li Xu ◽  
Guo Xin Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Mixed Model ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Speed Up ◽  
Neural Network Algorithm ◽  
Genetic Neural Network

In order to improve the efficiency and accuracy of the prediction of expressway traffic flow, this paper, based on the characteristics of the data of the expressway traffic flow, focuses on an optimized method of prediction with the application of the neural network with genetic algorithm. Applying genetic algorithm, optimizing BP neural network structure and establishing a new mixed model, this algorithm speed up the slow convergence velocity of traditional BP neural network prediction and increases the possibility to escape local minima. This algorithm based on the optimized genetic neural network predicts the actual data of the expressway traffic flow, the result of which shows that the application of the optimized method of prediction with the genetic neural network algorithm is effective and that it improves the rate and the accuracy of the prediction of the expressway traffic flow.

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.

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