scholarly journals Foreign Trade Export Forecast Based on Fuzzy Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yang Liu
Keyword(s):  
Neural Network ◽  
Foreign Trade ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Training Algorithms ◽  
Whole Process ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
The Index System ◽  
And Training

This article first analyzes the significance and methods of foreign trade export forecasting and determines the index system of foreign trade export forecasting by analyzing the results of foreign trade export forecasting research at home and abroad. Subsequently, the related concepts and principles of artificial neural network and fuzzy theory are explained, the types and training algorithms of the fuzzy neural network are introduced, and the neural network and fuzzy theory are combined to establish the prediction model. Finally, according to the characteristics of foreign trade exports, this article comprehensively considers the influence of various factors, applies the fuzzy neural network model to the foreign trade export forecast, introduces the whole process of the establishment of the fuzzy neural network forecasting model in detail, and predicts the change interval of foreign trade exports.

scholarly journals A Compound Controller of an Aerial Manipulator Based on Maxout Fuzzy Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xinchen Qi ◽  
Jianwei Wu ◽  
Jiansheng Pan
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Activation Function ◽  
Attitude Error ◽  
The Neural Network ◽  
Aerial Manipulator ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
The Stability

The aerial manipulator is a complex system with high coupling and instability. The motion of the robotic arm will affect the self-stabilizing accuracy of the unmanned aerial vehicles (UAVs). To enhance the stability of the aerial manipulator, a composite controller combining conventional proportion integration differentiation (PID) control, fuzzy theory, and neural network algorithm is proposed. By blurring the attitude error signal of UAV as the input of the neural network, the anti-interference ability and stability of UAV is improved. At the same time, a neural network model identifier based on Maxout activation function is built to realize accurate recognition of the controlled model. The simulation results show that, compared with the conventional PID controller, the composite controller combined with fuzzy neural network can improve the anti-interference ability and stability of UAV greatly.

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Pattern Recognition Controller Based on Fuzzy Neural Network

2014 ◽  
Vol 915-916 ◽  
pp. 1140-1143
Author(s):  
Liang Cheng
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Pattern Recognition ◽  
Fuzzy Neural Network ◽  
Feasible Solution ◽  
Fuzzy Theory ◽  
Network Design Problem ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Fuzzy Neural

A class of fuzzy neural network design problem H controller. By TS fuzzy theory, a model of nonlinear complex systems. Then, based on Lyapunov-Krasovskii functional and LMI technique, gives the design an H controller. By using the Matlab LMI toolbox, we can get the corresponding feasible solution of linear matrix inequalities. Finally, a numerical simulation examples are given to prove the correctness of the H controller.

Dynamic Controller Based on Ying Learning Algorithm for Medical Temperature System

2011 ◽  
Vol 187 ◽  
pp. 371-376
Author(s):  
Ping Zhang ◽  
Xiao Hong Hao ◽  
Heng Jie Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
The Novel ◽  
Fuzzy Neural ◽  
Learning Set ◽  
And Training ◽  
Novel Algorithm

In order to avoid the over fitting and training and solve the knowledge extraction problem in fuzzy neural networks system. Ying Learning Dynamic Fuzzy Neural Network (YL-DFNN) algorithm is proposed. The Learning Set based on K-VNN is constituted from message. Then the framework of is designed and its stability is proved. Finally, Simulation indicates that the novel algorithm is fast, compact, and capable in generalization.

Application of Fuzzy Neural Network to Analog Circuit Fault Diagnosis

2012 ◽  
Vol 182-183 ◽  
pp. 1179-1183 ◽  
Author(s):  
Shi Guan Zhou ◽  
Zai Fei Luo
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fuzzy Neural Network ◽  
Analog Circuit ◽  
Training Methods ◽  
Fuzzy Neural ◽  
Circuit Fault Diagnosis ◽  
Specific Implementation ◽  
The Mathematical Model ◽  
And Training

Considering the discreteness and non-linearity of the component parameter and the advancement and limitations of neural network in the analogous circuit fault diagnosis and as the combination of the fuzzy logic and neural network, the fuzzy neural network’s having the merits of both, involving learning, association, recognition, adaptation and fuzzy information processing, a method with fuzzy neural network for the analogous circuit fault diagnosis is proposed. In this paper, the structure and training methods of the fuzzy neural network are presented and the specific implementation of the diagnosis system is illustrated with examples. Simulation results show that the mathematical model has a better diagnostic effect. Compared with other methods, this diagnostic method, with the broad application prospect of its structure and method, is scientific, simple, and practical and so on.

THE PROBLEM OF MODELLING A TRUNK AIR ROUTE NETWORK

Aviation ◽  
2013 ◽  
Vol 17 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Tatiana Tseytlina ◽  
Victor Balashov ◽  
Andrey Smirnov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Development Environment ◽  
Route Network ◽  
Information Models ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Neural Network Toolbox

In this work we developed a fuzzy neural network-based model of the conditions for the existence of air routes, i.e. the rules underlying the emergence, existence and elimination of air routes (direct links between cities). The model belongs to the class of information models: the existence or non-existence of an air route is considered dependent on a complex of parameters. These parameters characterise the transport link, as well as the generational and target capabilities of the connected cities. The model was constructed using genetic algorithm techniques and self-organising Kohonen maps (implemented by software features of the STATISTICA package), as well as software tools of the Fuzzy Logic Toolbox and the Neural Network Toolbox of the MatLab development environment. The model is used to forecast the development of the topology of the network. The forecast is a necessary component of long-term forecasts of demand in the aircraft market.

Condenser Fault Diagnosis Based on FNN and Data Fusion

2010 ◽  
Vol 44-47 ◽  
pp. 3762-3766 ◽  
Author(s):  
Fei Xia ◽  
Hao Zhang ◽  
Dao Gang Peng ◽  
Hui Li ◽  
Yi Kang Su
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Fuzzy Neural Network ◽  
Membership Functions ◽  
Fusion Technology ◽  
New Approach ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
General Method

In order to improve the fault diagnosis result of the condenser, one new approach based on the fuzzy neural network and data fusion is proposed in this paper. Firstly, the data from the various sensors can be processed through the specific membership functions. With the fault symptoms and fault types of condenser, the fuzzy neural network is constructed for the primary fault diagnosis. Some likelihood of the neural network outputs is too close to make the correct decision of fault diagnosis. The problem can be solved by the data fusion technology. This method was successfully adopted in the application of condenser fault diagnosis. Compared with the general method of FNN, this approach can enhance the accuracy in the domain of fault diagnosis, especially for reducing the uncertainty in the fault diagnosis.

Study of Electricity Powered Four-Wheel Steering System Based on Fuzzy Neural Network

2014 ◽  
Vol 716-717 ◽  
pp. 1494-1499
Author(s):  
Wei Dong Li ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Steering System ◽  
Front Wheel ◽  
Slip Angle ◽  
Self Healing ◽  
Side Slip Angle ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Non Linear System

By the analysis of the operational principle of electricity powered four-wheel steering system, a new system based on the fuzzy neural network. Since this is a complex multivariate and non-linear system, by making use of the characteristics of fuzzy control and the neural network, a fuzzy neural network can be established. The speed of car and front-wheel steering angle being the input and steering model being the output, the side-slip angle of the in the process of steering can be control to zero. At last, by emulating this system with the software Matlab/Simulink, it shows that self-healing control technology can effectively control the side-slip angle and improve the motility and stability of a car.

Study of the Engineering Vehicles of Automatic Transmission Shift Schedule Based on Fuzzy Neural Network

2012 ◽  
Vol 591-593 ◽  
pp. 1720-1723 ◽  
Author(s):  
Yong Jing Huang ◽  
Jin Yao ◽  
Jia Hua Han ◽  
Di Wu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Automatic Transmission ◽  
Learning Ability ◽  
Shift Schedule ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Set Up ◽  
Takagi Sugeno ◽  
Self Learning

By combining the powerful self-learning ability of the neural network and the characteristic that the fuzzy control is designed based on the strategical rules of knowledge and language, this paper put forward the strategy of engineering vehicles' automatic transmission shift. According to a large number of experimental data, as well as the drivers' experience and the experts' profession, this paper put forward the strategy of engineering vehicles’ automatic transmission shift. The neural network model is set up based on Takagi-Sugeno and the factual cases are used to train and exam by MATLB, the simulation result showed that this method is feasible and meet the shift requirement, as it can accelerate effectively the establishment of the rules and reduce the set up time. The shift schedule can reflect precisely the actual out put target gear and meet the shift requirement.

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