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.

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.

Study on Data Fusion and Optimization Based on Neural Networks

2014 ◽  
Vol 530-531 ◽  
pp. 463-466 ◽  
Author(s):  
Liang Cheng
Keyword(s):  
Neural Network ◽  
Routing Protocol ◽  
Bp Neural Network ◽  
Network Routing ◽  
Original Model ◽  
Wireless Sensor ◽  
Sink Node ◽  
Data Set ◽  
Hierarchical Routing ◽  
Neural Network Algorithm

Due to limited energy wireless sensor networks, in order to extend the life cycle of WSN, the need for wireless sensor network routing protocol has been improved, based on the original model of hierarchical routing protocol LEACH algorithm, combined with efficient use of energy, new determine the clustering structure methods. The paper used in each sub-clan structures BP neural network algorithm for nodes preclude the data set is processed to reduce the amount of data transmitted to the sink node, reduce communication energy consumption to prolong the network lifetime goal.

scholarly journals Analysis of Color Language and Aesthetic Paradigm of Print Art Based on GB-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhiqiang Chen ◽  
Yongding Tan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Color Image ◽  
Subjective Evaluation ◽  
Basic Element ◽  
Language Analysis ◽  
Art Works ◽  
Different Types ◽  
Neural Network Algorithm ◽  
The Common

Color is the basic element of printmaking art creation and also an important medium for artists to express their emotions. In order to improve the understanding of the print by tourists, the research first carries out image analysis of different colors, summarizes the common color image adjectives in the art of publishing and painting, then optimizes BP neural network algorithm by gradient promotion (GP) algorithm, and constructs a color language analysis technology based on GP-BP neural network review paradigm. Through this technology, the content of color expression of different types of print art works is analyzed. Then, the subjective evaluation of these print art works is carried out by questionnaire adjustment method. Finally, the subjective evaluation and network evaluation results are compared. On the three materials, the expression effect of color image adjectives such as “heavy light fast,” “modern classical,” “lively steady,” and “soft Yang Gang” is consistent ( P value is greater than 0.05). The first set of print works mainly reflects the artist’s “affinity cold” emotion. The MSE values of the evaluation results are less than 0.01. That is, the color language analysis technology of print art based on GP-BP neural network can reflect the artist’s emotion to a certain extent when analyzing the color language of the print.

scholarly journals Application of artificial neural networks for irrigation control

2021 ◽  
pp. 84-88
Author(s):  
Galina Nickolaevna Kamyshova
Keyword(s):  
Neural Network ◽  
Irrigation Management ◽  
Small Error ◽  
The Neural Network ◽  
Different Types ◽  
Neural Network Algorithm ◽  
Irrigation Control ◽  
Artificial Neural ◽  
Spatio Temporal ◽  
Intelligent Methods

Modern methods of precision farming, based on the requirements of the spatio-temporal optimality of irrigation of agricultural crops, require new approaches, since achieving the required accuracy is impossible without the use of modern digital technologies and intelligent methods. The article presents a model of operational irrigation management based on an artificial neural network. The advantage is the small error of the neural network algorithm and its ability to adapt to changing conditions, in contrast to traditional methods, which makes it possible to provide optimal results for different types of soils and types of crops.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm
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