Name
Abstract | ||
|---|---|---|
Design of new neural networks is restricted due to some problems like stability, plasticity, computational complexity and memory consumption. These problems are overcome in the present work by using an interval feed-forward neural network (IFFNN). It has simple structure that reduces the computational complexity and memory consumption, and the use of Lyapunov stability (LS) based learning algorithm assures the stability. Effectiveness and applicability of the underlying IFFNN model is investigated on benchmark problems of identification. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICETET.2012.59 | ICETET |
Keywords | Field | DocType |
interval feed-forward neural network,lyapunov stability,memory consumption,underlying iffnn model,new neural network,present work,benchmark problem,simple structure,computational complexity,learning artificial intelligence,stability,plasticity,neural network,supervised learning | Feedforward neural network,Mathematical optimization,Computer science,Lyapunov stability,Recurrent neural network,Probabilistic neural network,Types of artificial neural networks,Time delay neural network,Artificial intelligence,Deep learning,Artificial neural network | Conference |
ISSN | Citations | PageRank |
2157-0477 | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Madhusudan Singh | 1 | 85 | 10.86 |
| Smriti Srivastava | 2 | 137 | 19.60 |