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.

A DELAYED NEURAL NETWORK METHOD FOR SOLVING CONVEX OPTIMIZATION PROBLEMS

2006 ◽  
Vol 16 (04) ◽  
pp. 295-303 ◽  
Author(s):  
YONGQING YANG ◽  
JINDE CAO
Keyword(s):  
Neural Network ◽  
Convex Programming ◽  
Global Exponential Stability ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Existence Of Solution ◽  
Convex Programming Problem ◽  
Convex Optimization Problems ◽  
Network Method ◽  
Projection Neural Network

In this paper, the delayed projection neural network for a class of solving convex programming problem is proposed. The existence of solution and global exponential stability of the proposed network are proved, which can guarantee to converge at an exact optimal solution of the convex programming problems. Several examples are given to show the effectiveness of the proposed network.

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.

A new projection neural network for linear and convex quadratic second-order cone programming

2021 ◽  
pp. 1-13
Author(s):  
Yaling Zhang ◽  
Hongwei Liu
Keyword(s):  
Neural Network ◽  
Lyapunov Function ◽  
Optimization Problems ◽  
Second Order ◽  
Cone Programming ◽  
Second Order Cone Programming ◽  
Force Optimization ◽  
Second Order Cone ◽  
Grasping Force ◽  
Projection Neural Network

A new projection neural network approach is presented for the linear and convex quadratic second-order cone programming. In the method, the optimal conditions of the linear and convex second-order cone programming are equivalent to the cone projection equations. A Lyapunov function is given based on the G-norm distance function. Based on the cone projection function, the descent direction of Lyapunov function is used to design the new projection neural network. For the proposed neural network, we give the Lyapunov stability analysis and prove the global convergence. Finally, some numerical examples and two kinds of grasping force optimization problems are used to test the efficiency of the proposed neural network. The simulation results show that the proposed neural network is efficient for solving some linear and convex quadratic second-order cone programming problems. Especially, the proposed neural network can overcome the oscillating trajectory of the exist projection neural network for some linear second-order cone programming examples and the min-max grasping force optimization problem.

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 Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1715
Author(s):  
Michele Alessandrini ◽  
Giorgio Biagetti ◽  
Paolo Crippa ◽  
Laura Falaschetti ◽  
Claudio Turchetti
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Network ◽  
Low Cost ◽  
Low Complexity ◽  
Human Activity Recognition ◽  
Motion Artifacts ◽  
Accelerometer Data

Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

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