Learning Dynamics of the Complex-Valued Neural Network in the Neighborhood of Singular Points
نویسنده
چکیده
In this paper, the singularity and its effect on learning dynamics in the complex-valued neural network are elucidated. It has learned that the linear combination structure in the updating rule of the complex-valued neural network increases the speed of moving away from the singular points, and the complex-valued neural network cannot be easily influenced by the singular points, whereas the learning of the usual real-valued neural network can be attracted in the neighborhood of singular points, which causes a standstill in learning. Simulation results on the learning dynamics of the three-layered real-valued and complex-valued neural networks in the neighborhood of singularities support the analytical results.
منابع مشابه
Complex-Valued Neurocomputing and Singular Points
Context: Recently, the singular points of neural networks have attracted attention from the artificial intelligence community, and their interesting properties have been demonstrated. The objective of this study is to provide an overview of studies on the singularities of complex-valued neural networks. Evidence Acquisition: This review is based on the relevant literature on complex-valued neur...
متن کاملConstruction of Neural Networks that Do Not Have Critical Points Based on Hierarchical Structure
a critical point is a point at which the derivatives of an error function are all zero. It has been shown in the literature that critical points caused by the hierarchical structure of a realvalued neural network (NN) can be local minima or saddle points, although most critical points caused by the hierarchical structure are saddle points in the case of complex-valued neural networks. Several s...
متن کاملNumerical solution of fuzzy differential equations under generalized differentiability by fuzzy neural network
In this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. Utilizing the Generalized characterization Theorem. Then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. Here neural network is considered as a part of large eld called ne...
متن کاملGENERATION OF MULTIPLE SPECTRUM-COMPATIBLE ARTIFICIAL EARTHQUAKE ACCELEGRAMS WITH HARTLEY TRANSFORM AND RBF NEURAL NETWORK
The Hartley transform, a real-valued alternative to the complex Fourier transform, is presented as an efficient tool for the analysis and simulation of earthquake accelerograms. This paper is introduced a novel method based on discrete Hartley transform (DHT) and radial basis function (RBF) neural network for generation of artificial earthquake accelerograms from specific target spectrums. Acce...
متن کاملNatural Gradient Descent for Training Stochastic Complex-Valued Neural Networks
In this paper, the natural gradient descent method for the multilayer stochastic complex-valued neural networks is considered, and the natural gradient is given for a single stochastic complex-valued neuron as an example. Since the space of the learnable parameters of stochastic complex-valued neural networks is not the Euclidean space but a curved manifold, the complex-valued natural gradient ...
متن کامل