Title :
A fast learning algorithm for adaptive linear combiner
Author :
Tan, Jun ; Cornett, Frank N.
Author_Institution :
Dept. of Electr. & Comput. Eng., Tennessee Technol. Univ., Cookeville, TN, USA
Abstract :
The paper suggests a learning algorithm for adaptive systems and perceptrons different from traditional learning algorithms The weight updating is kept with “instant” input and output signals. The convergence property is discussed. Also, several examples including system identification are given to show its high convergence speed compared with the LMS algorithm
Keywords :
adaptive signal processing; adaptive systems; identification; learning (artificial intelligence); least mean squares methods; perceptrons; LMS algorithm; adaptive linear combiner; adaptive systems; convergence property; fast learning algorithm; high convergence speed; input/output signals; perceptrons; system identification; weight updating; Adaptive systems; Algorithm design and analysis; Convergence; Differential equations; History; Least squares approximation; Neural networks; Stability; Steady-state; System identification;
Conference_Titel :
System Theory, 1997., Proceedings of the Twenty-Ninth Southeastern Symposium on
Conference_Location :
Cookeville, TN
Print_ISBN :
0-8186-7873-9
DOI :
10.1109/SSST.1997.581689
