Use of periodic and monotonic activation functions in multilayer feedforward neural networks trained by extended Kalman filter algorithm

2002 ◽  
Vol 149 (4) ◽  
pp. 217 ◽  
Author(s):  
K.-W. Wong ◽  
C.-S. Leung ◽  
S.-J. Chang
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Feedforward Neural Networks ◽  
Activation Functions ◽  
Kalman Filter Algorithm

An Augmented Extended Kalman Filter Algorithm for Complex-Valued Recurrent Neural Networks

2007 ◽  
Vol 19 (4) ◽  
pp. 1039-1055 ◽  
Author(s):  
Su Lee Goh ◽  
Danilo P. Mandic
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Recurrent Neural Networks ◽  
Adaptive Filters ◽  
General Complex ◽  
Recent Developments ◽  
Kalman Filter Algorithm ◽  
Fully Connected ◽  
Complex Valued

An augmented complex-valued extended Kalman filter (ACEKF) algorithm for the class of nonlinear adaptive filters realized as fully connected recurrent neural networks is introduced. This is achieved based on some recent developments in the so-called augmented complex statistics and the use of general fully complex nonlinear activation functions within the neurons. This makes the ACEKF suitable for processing general complex-valued nonlinear and nonstationary signals and also bivariate signals with strong component correlations. Simulations on benchmark and real-world complex-valued signals support the approach.

ACCELERATING TRAINING OF FEEDFORWARD NEURAL NETWORKS

1994 ◽  
Vol 03 (03) ◽  
pp. 339-348
Author(s):  
CARL G. LOONEY
Keyword(s):  
Neural Networks ◽  
Local Minimum ◽  
Random Search ◽  
Feedforward Neural Networks ◽  
Conjugate Gradients ◽  
Activation Functions ◽  
Problematic Behavior ◽  
Methods And Techniques ◽  
Adaptive Step ◽  
Quality Learning

We review methods and techniques for training feedforward neural networks that avoid problematic behavior, accelerate the convergence, and verify the training. Adaptive step gain, bipolar activation functions, and conjugate gradients are powerful stabilizers. Random search techniques circumvent the local minimum trap and avoid specialization due to overtraining. Testing assures quality learning.

The p53–Mdm2 regulation relationship under different radiation doses based on the continuous–discrete extended Kalman filter algorithm

2018 ◽  
Vol 273 ◽  
pp. 230-236 ◽  
Author(s):  
Yurong Li ◽  
Jun Chen ◽  
Li Jiang ◽  
Nianyin Zeng ◽  
Haiyan Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Radiation Doses ◽  
Kalman Filter Algorithm
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