Title :
High-order neural network structures for identification of dynamical systems
Author :
Kosmatopoulos, Elias B. ; Polycarpou, Marios M. ; Christodoulou, Manolis A. ; Ioannou, Petros A.
Author_Institution :
Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece
fDate :
3/1/1995 12:00:00 AM
Abstract :
Several continuous-time and discrete-time recurrent neural network models have been developed and applied to various engineering problems. One of the difficulties encountered in the application of recurrent networks is the derivation of efficient learning algorithms that also guarantee the stability of the overall system. This paper studies the approximation and learning properties of one class of recurrent networks, known as high-order neural networks; and applies these architectures to the identification of dynamical systems. In recurrent high-order neural networks, the dynamic components are distributed throughout the network in the form of dynamic neurons. It is shown that if enough high-order connections are allowed then this network is capable of approximating arbitrary dynamical systems. Identification schemes based on high-order network architectures are designed and analyzed
Keywords :
identification; learning (artificial intelligence); neural net architecture; recurrent neural nets; stability; approximation properties; continuous-time recurrent neural network models; discrete-time recurrent neural network models; dynamic neurons; dynamical systems identification; efficient learning algorithms; engineering problems; high-order connections; high-order neural network structures; learning properties; overall system stability; Algorithm design and analysis; Feedforward neural networks; Helium; Multi-layer neural network; Neural networks; Neurofeedback; Neurons; Recurrent neural networks; Stability analysis; Transfer functions;
Journal_Title :
Neural Networks, IEEE Transactions on
