Complex Valued Neural Network is one of the open topics in the machine learning society. In this paper we will try to go through the problems of the complex valued neural networks gradients computations by combining the global and local optimization algorithms. The outcome of the current research is the combined global-local algorithm for training the complex valued feed forward neural network ...

In view of many applications, in recent years, there has been increasing interest in complex valued neural networks. In this paper, it is reasoned that transforming real valued signals into complex valued signals (using Discrete Fourier Transform) and processing them in that domain is equivalent to processing real valued signals. This approach could have many advantages. Also neural networks ba...

C OMPLEX-VALUED neural networks (CVNNs) exhibit very desirable characteristics in their learning, self-organizing, and processing dynamics, which makes them attractive for applications in various areas in science and technology. For example, they are perfectly suited to deal with complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems dealing wi...

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 lea...

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...

Complex-valued neural networks (CVNNs) are an emerging field of research in neural networks due to their potential representational properties for audio, image, and physiological signals. It is common in signal processing to transform sequences of real values to the complex domain via a set of complex basis functions, such as the Fourier transform. We show how CVNNs can be used to learn complex...

Different from gradient-based neural networks (in short, gradient neural networks), a special kind of recurrent neural networks has recently been proposed by Zhang et al for time-varying matrix inversion and equations solving. As compared to gradient neural networks (GNN), Zhang neural networks (ZNN) are designed based on matrix-valued or vector-valued error functions, instead of scalar-valued ...