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Conjugate gradient method has been verified to be one effective strategy for training neural networks due to its low memory requirements and fast convergence. In this paper, we propose an efficient conjugate gradient method to train fully complex-valued network models in terms of Wirtinger differential operator. Two ways are adopted to enhance the training performance. One is to construct a sufficient descent direction during training by designing a fine tuning conjugate coefficient. Another technique is to pursue the optimal learning rate instead of a fixed constant in each iteration which is determined by employing a generalized Armijo search. In addition, we rigorously prove its weak and strong convergence results, i.e., the gradient norms of objective function with respect to weights approach zero along with the increasing iterations and the weight sequence tends to the optimal point. To verify the effectiveness and rationality of the proposed method, four illustrated simulations have been performed on both typical regression and classification problems.
This article was published in the following journal.
Name: Neural networks : the official journal of the International Neural Network Society
Like most machine learning algorithms, Echo State Networks possess several hyperparameters that have to be carefully tuned for achieving best performance. For minimizing the error on a specific task, ...
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Tinnitus is the occurrence of an auditory sensation without the presence of an acoustic stimulus. Approximately, 50 million people in the United States experience chronic tinnitus and 15 m...
The aim of the study is to demonstrate that our semantic knowledge (elements of our long-term memory and the process we use them) respond to a graphic organisation and gather together foll...
The overarching goal of this research program is to elucidate causal and directional neural network- level abnormalities in depression, and how they are modulated by an individually-tailor...
The primary objective of the study will be to compare intraoperative post TOF repair RVOT gradient under two different anaesthetic depths. Secondary objectives will be to follow up change ...
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
Complex sets of enzymatic reactions connected to each other via their product and substrate metabolites.
Separation of particles according to density by employing a gradient of varying densities. At equilibrium each particle settles in the gradient at a point equal to its density. (McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed)
A technique used to separate particles according to their densities in a continuous density gradient. The sample is usually mixed with a solution of known gradient materials and subjected to centrifugation. Each particle sediments to the position at which the gradient density is equal to its own. The range of the density gradient is usually greater than that of the sample particles. It is used in purifying biological materials such as proteins, nucleic acids, organelles, and cell types.
An early embryonic developmental process of CHORDATES that is characterized by morphogenic movements of ECTODERM resulting in the formation of the NEURAL PLATE; the NEURAL CREST; and the NEURAL TUBE. Improper closure of the NEURAL GROOVE results in congenital NEURAL TUBE DEFECTS.