scholarly journals Estimation on Domain of Attraction and Convergence Rate of Hopfield Continuous Feedback Neural Networks

2001 ◽  
Vol 62 (3) ◽  
pp. 528-534 ◽  
Author(s):  
Jinde Cao ◽  
Qing Tao
Keyword(s):  
Neural Networks ◽  
Convergence Rate ◽  
Domain Of Attraction ◽  
Continuous Feedback ◽  
Feedback Neural Networks

Applying Neural Networks for Modeling of Financial Assets

2018 ◽  
pp. 172-204
Author(s):  
Dmitry Averchenko ◽  
Artem Aldyrev
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Standard Deviation ◽  
Learning Process ◽  
Moving Average ◽  
Back Propagation ◽  
Financial Assets ◽  
Movement Index ◽  
Analytical System ◽  
Feedback Neural Networks

The purpose of this chapter is to develop an analytical system for forecasting prices of financial assets with the use of artificial neural networks technology. Proposed by the authors, the analytical system consists of several neural networks, each of which makes the forecast of financial assets prices. The system includes recurrence (with feedback) neural networks with sigmoidal activation formula. This allows the networks to “remember” a sequence of reactions to the same stimulus. The learning process of neural networks is performed using an algorithm of back propagation of error. The key parameters of forecast for this analytical system are the indicators presented by the terminal MetaTrader 4-broker Forex Club: Average Directional и Movement Index; Bollinger Bands; Envelopes; Ichimoku Kinko Hyo; Moving Average; Parabolic SAR; Standard Deviation; Average True Range; and others.

A self-tuning trajectory tracking controller for wheeled mobile robots

2019 ◽  
Vol 46 (6) ◽  
pp. 828-838 ◽  
Author(s):  
Pouya Panahandeh ◽  
Khalil Alipour ◽  
Bahram Tarvirdizadeh ◽  
Alireza Hadi
Keyword(s):  
Neural Networks ◽  
Convergence Rate ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Suggested Method ◽  
Wheeled Mobile Robots ◽  
Trajectory Tracking Control ◽  
Content Type ◽  
Tracking Controller ◽  
Trajectory Tracking Controller

Purpose Trajectory tracking is a common problem in the field of mobile robots which has attracted a lot of attention in the past two decades. Therefore, besides the search for new controllers to achieve a better performance, improvement and optimization of existing control rules are necessary. Trajectory tracking control laws usually contain constant gains which affect greatly the robot’s performance. Design/methodology/approach In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller. Findings Simulations and experiments are performed to assess the ability of the suggested scheme. The obtained results show the effectiveness of the proposed method. Originality/value In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller.

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