A new method based on principal component analysis (PCA) and artificial neural networks (ANN) is proposed for fault diagnosis of gearboxes. Firstly the six different base wavelets are considered, in which three are from real valued and other three from complex valued. Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared...

The usual real-valued artificial neural networks have been applied to various fields such as telecommunications, robotics, bioinformatics, image processing and speech recognition, in which complex numbers (two dimensions) are often used with the Fourier transformation. This indicates the usefulness of complex-valued neural networks whose input and output signals and parameters such as weights a...

Abstract Experiment of time sequential pulse train synthesis using a layered and partially recurrent complex valued neural network is reported A half of the three layer complex valued neural network is used to generate sinusoidal oscillation and the other half to synthesize adaptively the intended pulse shapes and sequences Stable time sequential pulse signals are obtained after completion of l...

A vector matrix real time backpropagation algorithm for recurrent neural networks that approximate multi-valued periodic functions," Received Unlike feedforward neural networks (FFNN) which can act as universal function ap-proximators, recursive, or recurrent, neural networks can act as universal approximators for multi-valued functions. In this paper, a real time recursive backpropagation (RTR...

Applications of complex valued neural networks to optical signal processing using phase sensitive detection schemes are proposed and discussed In optical information processing systems the advantages of complex valued neural networks are realized directly utilizing physical phenomena of lightwave and phase sensitive optical detection In the proposed system the summation and nonlinear transform ...

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...

Two-layered neural networks are well known as autoencoders (AEs) in order to reduce the dimensionality of data. AEs are successfully employed as pre-trained layers of neural networks for classification tasks. Most of the existing studies conceived real-valued AEs in real-valued neural networks. This study investigated complexand quaternion-valued AEs for complexand quaternion-valued neural netw...