An Individual Adaptive Gain Parameter Backpropagation Algorithm for Complex-Valued Neural Networks

2006 ◽  
pp. 551-557 ◽  
Author(s):  
Songsong Li ◽  
Toshimi Okada ◽  
Xiaoming Chen ◽  
Zheng Tang
Keyword(s):  
Neural Networks ◽  
Backpropagation Algorithm ◽  
Gain Parameter ◽  
Complex Valued

scholarly journals Flexible Transmitter Network

2021 ◽  
pp. 1-20
Author(s):  
Shao-Qun Zhang ◽  
Zhi-Hua Zhou
Keyword(s):  
Neural Networks ◽  
Building Block ◽  
Neuron Model ◽  
Activation Function ◽  
Spatiotemporal Data ◽  
Basic Building Block ◽  
Backpropagation Algorithm ◽  
Gradient Calculation ◽  
Fully Connected ◽  
Complex Valued

Abstract Current neural networks are mostly built on the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. This letter proposes the flexible transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. The FT model employs a pair of parameters to model the neurotransmitters between neurons and puts up a neuron-exclusive variable to record the regulated neurotrophin density. Thus, the FT model can be formulated as a two-variable, two-valued function, taking the commonly used MP neuron model as its particular case. This modeling manner makes the FT model biologically more realistic and capable of handling complicated data, even spatiotemporal data. To exhibit its power and potential, we present the flexible transmitter network (FTNet), which is built on the most common fully connected feedforward architecture taking the FT model as the basic building block. FTNet allows gradient calculation and can be implemented by an improved backpropagation algorithm in the complex-valued domain. Experiments on a broad range of tasks show that FTNet has power and potential in processing spatiotemporal data. This study provides an alternative basic building block in neural networks and exhibits the feasibility of developing artificial neural networks with neuronal plasticity.

scholarly journals Convergence of Batch Split-Complex Backpropagation Algorithm for Complex-Valued Neural Networks

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
Huisheng Zhang ◽  
Chao Zhang ◽  
Wei Wu
Keyword(s):  
Neural Networks ◽  
Theoretical Analysis ◽  
Error Function ◽  
Iteration Process ◽  
Learning Rate ◽  
Backpropagation Algorithm ◽  
Numerical Example ◽  
Moderate Condition ◽  
Complex Valued

The batch split-complex backpropagation (BSCBP) algorithm for training complex-valued neural networks is considered. For constant learning rate, it is proved that the error function of BSCBP algorithm is monotone during the training iteration process, and the gradient of the error function tends to zero. By adding a moderate condition, the weights sequence itself is also proved to be convergent. A numerical example is given to support the theoretical analysis.

A MODIFIED ERROR BACKPROPAGATION ALGORITHM FOR COMPLEX-VALUE NEURAL NETWORKS

2005 ◽  
Vol 15 (06) ◽  
pp. 435-443 ◽  
Author(s):  
XIAOMING CHEN ◽  
ZHENG TANG ◽  
CATHERINE VARIAPPAN ◽  
SONGSONG LI ◽  
TOSHIMI OKADA
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Fourier Transformation ◽  
Error Function ◽  
Local Minima ◽  
Backpropagation Algorithm ◽  
Speed Up ◽  
Simulation Results ◽  
Hidden Layer ◽  
Complex Valued

The complex-valued backpropagation algorithm has been widely used in fields of dealing with telecommunications, speech recognition and image processing with Fourier transformation. However, the local minima problem usually occurs in the process of learning. To solve this problem and to speed up the learning process, we propose a modified error function by adding a term to the conventional error function, which is corresponding to the hidden layer error. The simulation results show that the proposed algorithm is capable of preventing the learning from sticking into the local minima and of speeding up the learning.

Complex-valued Neural Networks

2011 ◽  
Vol 131 (1) ◽  
pp. 2-8
Author(s):  
Akira Hirose
Keyword(s):  
Neural Networks ◽  
Complex Valued

Stochastic Complex-valued Neural Networks for Radar

2021 ◽  
Author(s):  
Othmane-Latif Ouabi ◽  
Radmila Pribic ◽  
Sorin Olaru
Keyword(s):  
Neural Networks ◽  
Complex Valued

scholarly journals Finite-time projective synchronization of stochastic complex-valued neural networks with probabilistic time-varying delays

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Meng Hui ◽  
Jiahuang Zhang ◽  
Jiao Zhang ◽  
Herbert Ho-Ching Iu ◽  
Rui Yao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Projective Synchronization ◽  
Time Varying ◽  
Complex Valued
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