Convergence of an Online Split-Complex Gradient Algorithm for Complex-Valued Neural Networks
نویسندگان
چکیده
The online gradient method has been widely used in training neural networks. We consider in this paper an online split-complex gradient algorithm for complex-valued neural networks. We choose an adaptive learning rate during the training procedure. Under certain conditions, by firstly showing the monotonicity of the error function, it is proved that the gradient of the error function tends to zero and the weight sequence tends to a fixed point. A numerical example is given to support the theoretical findings.
منابع مشابه
Convergence of Batch Split-Complex Backpropagation Algorithm for Complex-Valued Neural Networks
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. ...
متن کاملA Fully Complex-Valued Radial Basis Function Network and its Learning Algorithm
In this paper, a fully complex-valued radial basis function (FC-RBF) network with a fully complex-valued activation function has been proposed, and its complex-valued gradient descent learning algorithm has been developed. The fully complex activation function, sech(.) of the proposed network, satisfies all the properties needed for a complex-valued activation function and has Gaussian-like cha...
متن کاملAn Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking
To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite ti...
متن کاملConvergence of Split-Complex Backpropagation Algorithm with Momentum∗
This paper investigates a split-complex backpropagation algorithm with momentum (SCBPM) for complex-valued neural networks. Some convergence results for SCBPM are proved under relaxed conditions compared with existing results. The monotonicity of the error function during the training iteration process is also guaranteed. Two numerical examples are given to support the theoretical findings.
متن کاملA Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers
o enhance the performances of rough-neural networks (R-NNs) in the system identification, on the base of emotional learning, a new stable learning algorithm is developed for them. This algorithm facilitates the error convergence by increasing the memory depth of R-NNs. To this end, an emotional signal as a linear combination of identification error and its differences is used to achie...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010