The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Ultra High Frequency Sigmoid and Trigonometric Higher Order Neural Networks for Data Pattern Recognition
Abstract
This chapter develops a new nonlinear model, Ultra high frequency siGmoid and Trigonometric Higher Order Neural Networks (UGT-HONN), for data pattern recognition. UGT-HONN includes Ultra high frequency siGmoid and Sine function Higher Order Neural Networks (UGS-HONN) and Ultra high frequency siGmoid and Cosine functions Higher Order Neural Networks (UGC-HONN). UGS-HONN and UGC-HONN models are used to recognition data patterns. Results show that UGS-HONN and UGC-HONN models are better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models, since UGS-HONN and UGC-HONN models to recognize data pattern with error approaching 0.0000%.
Related Content
P. Chitra, A. Saleem Raja, V. Sivakumar.
© 2024.
24 pages.
|
K. Ezhilarasan, K. Somasundaram, T. Kalaiselvi, Praveenkumar Somasundaram, S. Karthigai Selvi, A. Jeevarekha.
© 2024.
36 pages.
|
Kande Archana, V. Kamakshi Prasad, M. Ashok.
© 2024.
17 pages.
|
Ritesh Kumar Jain, Kamal Kant Hiran.
© 2024.
23 pages.
|
U. Vignesh, R. Elakya.
© 2024.
13 pages.
|
S. Karthigai Selvi, R. Siva Shankar, K. Ezhilarasan.
© 2024.
16 pages.
|
Vemasani Varshini, Maheswari Raja, Sharath Kumar Jagannathan.
© 2024.
20 pages.
|
|
|