Title :
Design of an Interval Feed-Forward Neural Network
Author :
Srivastava, Sanjeev ; Singh, Monika
Author_Institution :
Dept. of Instrum. & Control Eng., Netaji Subhas Inst. of Technol., New Delhi, India
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.
Keywords :
Lyapunov methods; computational complexity; feedforward neural nets; learning (artificial intelligence); plasticity; stability; IFFNN; LS-based learning algorithm; Lyapunov stability based learning algorithm; computational complexity reduction; interval feedforward neural network design; memory consumption reduction; plasticity; Lyapunov stability and identification; Neural network; supervised learning;
Conference_Titel :
Emerging Trends in Engineering and Technology (ICETET), 2012 Fifth International Conference on
Conference_Location :
Himeji
Print_ISBN :
978-1-4799-0276-7
DOI :
10.1109/ICETET.2012.59
