In this paper we propose a novel technique for imagebased synchronization of two mobile received TV sequences having different spatial resolutions, deviant image qualities, and possibly lost image blocks. The sequences are aligned spatially and temporally based on an affine transform. The optimal transformation parameters are determined numerically by the LevenbergMarquardt-Algorithm due to com...

The success of an Artificial Neural Network (ANN) strongly depends on its training process. Gradient-based techniques have been satisfactorily used in the ANN training. However, in many cases, these algorithms are very slow and susceptible to the local minimum problem. In our work, we implemented a hybrid learning algorithm that integrates Genetic Algorithms(GAs) and the LevenbergMarquardt(LM) ...

-This paper focuses the function approximation capability of feed forward neural network (FFNN). A Graphical user Interface (GUI) system has been developed and tested for function approximation. This GUI system can approximate any nonlinear/linear function which can have any number of input variable and six output variables. Configuration of neural network can be set from a single GUI window. A...

The paper describes a Neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neurofuzzy network efficiently. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared ...

We describe a generalized Levenberg-Marquardt method for computing critical points of the Ginzburg-Landau energy functional which models superconductivity. The algorithm is a blend of a Newton iteration with a Sobolev gradient descent method, and is equivalent to a trust-region method in which the trustregion radius is defined by a Sobolev metric. Numerical test results demonstrate the method t...

In this paper, we propose a new updating rule of the LevenbergMarquardt (LM) parameter for the LM method for nonlinear equations. We show that the global complexity bound of the new LM algorithm is O( −2), that is, it requires at most O( −2) iterations to derive the norm of the gradient of the merit function below the desired accuracy . Host: Jiawang Nie Wednesday, November 1, 2017 4:00 PM AP&M...

This paper reviews various optimization techniques available for training multi-layer perception (MLP) artificial neural networks for compression of images. These optimization techniques can be classified into two categories: Derivative-based and Derivative free optimization. The former is based on the calculation of gradient and includes Gradient Descent, Conjugate gradient, Quasi-Newton, Leve...

We propose a new self-adaptive Levenberg-Marquardt algorithm for the system of nonlinear equations F(x) = 0. The Levenberg-Marquardt parameter is chosen as the product of ‖Fk‖ with δ being a positive constant, and some function of the ratio between the actual reduction and predicted reduction of the merit function. Under the local error bound condition which is weaker than the nonsingularity, w...

The performance of Newton-Raphson, LevenbergMarquardt, Damped Newton-Raphson and genetic algorithms are investigated for the estimation of induction motor equivalent circuit parameters from commonly available manufacturer data. A new hybrid algorithm is then proposed that combines the advantages of both descent and natural optimisation algorithms. Through computer simulation, the hybrid algorit...

For solving nonsmooth systems of equations, the Levenberg-Marquardt method and its variants are of particular importance because of their locally fast convergent rates. Finitely manymaximum functions systems are very useful in the study of nonlinear complementarity problems, variational inequality problems, Karush-Kuhn-Tucker systems of nonlinear programming problems, and many problems in mecha...