Semi-physical neural modeling for linear signal restoration
نویسندگان
چکیده
منابع مشابه
Tools for Semi-physical Modeling
By semi-physical modeling we mean such an application of system identiication, where physical insight into the application is used to come up with suitable nonlinear transformations of the raw measurements, so as to allow for a good model structure. Semi-physical modeling is less "ambitious" than physical modeling, in that no complete physical structure is sought, just suitable inputs and outpu...
متن کاملSolving Linear Semi-Infinite Programming Problems Using Recurrent Neural Networks
Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem. Then, we use...
متن کاملAnother stopping rule for linear iterative signal restoration
A new stopping rule is proposed for linear, iterative signal restoration using the gradient descent and conjugate gradient algorithms. The stopping rule attempts to minimize MSE under the assumption that the signal arises from a white noise process. This assumption is appropriate for many coherent imaging applications. The stopping rule is trivial to compute, and for xed relaxation parameters, ...
متن کاملSemi-blind image restoration using a local neural approach
This work aims to define and experimentally evaluate an iterative strategy based on neural learning for semi-blind image restoration in the presence of blur and noise. A salient aspect of our solution is the local estimation of the restored image based on gradient descent strategies. This method can be viewed as a neural strategy where the pixels of the restored image are the synapse’s weights ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Networks
سال: 2013
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2012.12.003