scholarly journals Neural Network to Solve Concave Games

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Zixin Liu ◽  
Nengfa Wang
Keyword(s):  
Neural Network ◽  
Variational Inequality ◽  
Stable Theory ◽  
Ky Fan Inequality ◽  
Network Method ◽  
Projection Equation ◽  
Simulation Results ◽  
Ky Fan ◽  
Stability Results ◽  
Theoretical Results

The issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concave games are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Finally, two classic games’ simulation results are given to illustrate the theoretical results.

Creep Behavior Prediction Research of T92 Steel Based on BP Neural Network

2013 ◽  
Vol 467 ◽  
pp. 203-207
Author(s):  
Jian Liu
Keyword(s):  
Neural Network ◽  
Creep Behavior ◽  
Bp Neural Network ◽  
Network Theory ◽  
Experimental Results ◽  
Behavior Prediction ◽  
Forecast Precision ◽  
Network Method ◽  
Simulation Results ◽  
Neural Network Theory

Based on the BP neural network theory, the creep rate prediction model of T92 steel was established under multiple stress levels. Obtained the experimental results and using the model, the experimental results were trained. The results show that the simulation results match the measured results well with a high forecast precision. The BP neural network method can serve as research on T92 steel creep behavior.

scholarly journals A One-Layer Recurrent Neural Network for Solving Pseudoconvex Optimization with Box Set Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Huaiqin Wu ◽  
Rong Yao ◽  
Ruoxia Li ◽  
Xiaowei Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Network ◽  
Nonsmooth Analysis ◽  
Feasible Region ◽  
Stable Theory ◽  
Box Constraints ◽  
Set Constraints ◽  
Analysis Technique ◽  
Theoretical Results

A one-layer recurrent neural network is developed to solve pseudoconvex optimization with box constraints. Compared with the existing neural networks for solving pseudoconvex optimization, the proposed neural network has a wider domain for implementation. Based on Lyapunov stable theory, the proposed neural network is proved to be stable in the sense of Lyapunov. By applying Clarke’s nonsmooth analysis technique, the finite-time state convergence to the feasible region defined by the constraint conditions is also addressed. Illustrative examples further show the correctness of the theoretical results.

scholarly journals A Projection Neural Network for Circular Cone Programming

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yaling Zhang ◽  
Hongwei Liu
Keyword(s):  
Neural Network ◽  
Energy Function ◽  
Computation Time ◽  
Low Complexity ◽  
Circular Cone ◽  
State Variables ◽  
Cone Programming ◽  
Network Method ◽  
Projection Equation ◽  
Projection Neural Network

A projection neural network method for circular cone programming is proposed. In the KKT condition for the circular cone programming, the complementary slack equation is transformed into an equivalent projection equation. The energy function is constructed by the distance function and the dynamic differential equation is given by the descent direction of the energy function. Since the projection on the circular cone is simple and costs less computation time, the proposed neural network requires less state variables and leads to low complexity. We prove that the proposed neural network is stable in the sense of Lyapunov and globally convergent. The simulation experiments show our method is efficient and effective.

Identifying Transformer Inrush Current Based on Artificial Neural Network

2011 ◽  
Vol 58-60 ◽  
pp. 1779-1785
Author(s):  
Hang Xu ◽  
Xu Hong Yang ◽  
Jian Hua Ye ◽  
Hong Qian ◽  
Yang Xue ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Accuracy ◽  
Inrush Current ◽  
Fault Current ◽  
Neural Network Method ◽  
Magnetizing Inrush Current ◽  
Network Method ◽  
Simulation Results ◽  
Digital Simulations

In this paper, lots of digital simulations of transformer magnetizing inrush current and fault current have been done through Matlab software. A neural network method used to identify magnetizing inrush current has been proposed and established. The simulation results show that this method can identify magnetizing inrush current well, with high accuracy.

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

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