The stability analysis for a novel feedback neural network with partial connection

2013 ◽  
Vol 116 ◽  
pp. 22-29 ◽  
Author(s):  
Didi Wang ◽  
Pei-Chann Chang ◽  
Li Zhang ◽  
Jheng-Long Wu ◽  
Changle Zhou
Keyword(s):  
Neural Network ◽  
Stability Analysis ◽  
Feedback Neural Network ◽  
The Stability ◽  
Partial Connection

Neural hybrid control of manipulators, stability analysis

Robotica ◽  
2001 ◽  
Vol 19 (1) ◽  
pp. 41-51 ◽  
Author(s):  
N. Saadia ◽  
Y. Amirat ◽  
J. Pontnau ◽  
N.K. M'Sirdi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Stability Analysis ◽  
Position Control ◽  
Hybrid Control ◽  
Control Structures ◽  
Neural Network Controller ◽  
Artificial Neural ◽  
The Stability

The design and implementation of adaptive control for nonlinear unknown systems is extremely difficult. The nonlinear adaptive control for assembly robots performing a peg-in-hole insertion is one such an example. The recently intensively studied neural networks brings a new stage in the development of adaptive control, particularly for unknown nonlinear systems. The aim of this paper is to propose a new approach of hybrid force position control of an assembly robot based on artificial neural networks systems. An appropriate neural network is used to model the plant and is updated online. An artificial neural network controller is then directly evaluated using the updated neuro model. Two control structures are proposed and the stability analysis of the closed-loop system is investigated using the Lyapunov method. Experimental results demonstrate that the identification and control schemes suggested in this paper are efficient in practice.

The Application of BP Neural Network in the Research of the Landslide Stability

2012 ◽  
Vol 594-597 ◽  
pp. 2290-2295
Author(s):  
Peng Li Li ◽  
Wei Ping Tian ◽  
Jia Chun Li
Keyword(s):  
Neural Network ◽  
Stability Analysis ◽  
Slope Stability ◽  
Bp Neural Network ◽  
Stability Evaluation ◽  
Human Engineering ◽  
Advantages And Disadvantages ◽  
Landslide Stability ◽  
Analysis Of Stability ◽  
The Stability

According to the basis of analysis of stability evaluation and prediction of landslide research status, discusses the factors influencing the stability of landslide and slope stability analysis methods; analyzing advantages and disadvantages of neural network research method, operability of evaluation of landslide stability analysis, select average slope, slope of the invading front, sliding surface slope, the maximum annual average rainfall, the recent slide situation, human engineering activities as indicators of slope stability evaluation， has established based on BP neural network landslide stability analyses and applying Matlab toolbox to train network. The error analysis result indicate the sample training result and the actual situation tally basically, prove this model can tally with the project reality.

Stability Analysis of the Nanjung Water Diversion Twin Tunnels based on Convergence Measurement

2019 ◽  
Vol 1 (1) ◽  
pp. 49-60
Author(s):  
Simon Heru Prassetyo ◽  
Ganda Marihot Simangunsong ◽  
Ridho Kresna Wattimena ◽  
Made Astawa Rai ◽  
Irwandy Arif ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Convergence Rate ◽  
Critical Strain ◽  
Convergence Rates ◽  
Strain Analysis ◽  
Water Diversion ◽  
Tunnel Face ◽  
Tunnel Stability ◽  
The Stability ◽  
Twin Tunnels

This paper focuses on the stability analysis of the Nanjung Water Diversion Twin Tunnels using convergence measurement. The Nanjung Tunnel is horseshoe-shaped in cross-section, 10.2 m x 9.2 m in dimension, and 230 m in length. The location of the tunnel is in Curug Jompong, Margaasih Subdistrict, Bandung. Convergence monitoring was done for 144 days between February 18 and July 11, 2019. The results of the convergence measurement were recorded and plotted into the curves of convergence vs. day and convergence vs. distance from tunnel face. From these plots, the continuity of the convergence and the convergence rate in the tunnel roof and wall were then analyzed. The convergence rates from each tunnel were also compared to empirical values to determine the level of tunnel stability. In general, the trend of convergence rate shows that the Nanjung Tunnel is stable without any indication of instability. Although there was a spike in the convergence rate at several STA in the measured span, that spike was not replicated by the convergence rate in the other measured spans and it was not continuous. The stability of the Nanjung Tunnel is also confirmed from the critical strain analysis, in which most of the STA measured have strain magnitudes located below the critical strain line and are less than 1%.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

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