Input-to-state stability (ISS) analysis for dynamic neural networks

1999 ◽  
Vol 46 (11) ◽  
pp. 1395-1398 ◽  
Author(s):  
E.N. Sanchez ◽  
J.P. Perez
Keyword(s):  
Neural Networks ◽  
Dynamic Neural Networks

scholarly journals A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Choon Ki Ahn
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Exponential Stability ◽  
External Disturbance ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Input Output ◽  
Lmi Approach ◽  
Dynamic Neural Networks ◽  
Numerical Examples

A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

A methodology for modeling batch reactors using generalized dynamic neural networks

2010 ◽  
Vol 159 (1-3) ◽  
pp. 195-202 ◽  
Author(s):  
Moein Navvab Kashani ◽  
Shahrokh Shahhosseini
Keyword(s):  
Neural Networks ◽  
Batch Reactors ◽  
Dynamic Neural Networks

Optimization of Dynamic Neural Network Performance for Short-Term Traffic Prediction

2003 ◽  
Vol 1836 (1) ◽  
pp. 45-56 ◽  
Author(s):  
Sherif Ishak ◽  
Prashanth Kotha ◽  
Ciprian Alecsandru
Keyword(s):  
Neural Networks ◽  
Network Performance ◽  
Information Dissemination ◽  
Target Location ◽  
Prediction Performance ◽  
Time Factor ◽  
Traffic Prediction ◽  
Short Term ◽  
Traffic Condition ◽  
Dynamic Neural Networks

An approach is presented for optimizing short-term traffic-prediction performance by using multiple topologies of dynamic neural networks and various network-related and traffic-related settings. The conducted study emphasized the potential benefit of optimizing the prediction performance by deploying multimodel approaches under parameters and traffic-condition settings. Emphasis was placed on the application of temporal-processing topologies in short-term speed predictions in the range of 5-min to 20-min horizons. Three network topologies were used: Jordan–Elman networks, partially recurrent networks, and time-lagged feedforward networks. The input patterns were constructed from data collected at the target location and at upstream and downstream locations. However, various combinations were also considered. To encourage the networks to associate with historical information on recurrent conditions, a time factor was attached to the input patterns to introduce time-recognition capabilities, in addition to information encoded in the recent past data. The optimal prediction settings (type of topology and input settings) were determined so that performance was maximized under different traffic conditions at the target and adjacent locations. The optimized performance of the dynamic neural networks was compared to that of a statistical nonlinear time series approach, which was outperformed in most cases. The study showed that no single topology consistently outperformed the others for all prediction horizons considered. However, the results showed that the significance of introducing the time factor was more pronounced under longer prediction horizons. A comparative evaluation of performance of optimal and nonoptimal settings showed substantial improvement in most cases. The applied procedure can also be used to identify the prediction reliability of information-dissemination systems.

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