Gaussian decomposition of images leads to many promising applications in computer graphics. Gaussian representations can be used for image smoothing, motion analysis, and feature selection for image recognition. Furthermore, image construction from a Gaussian representation is fast, since the Gaussians only need to be added together. The most optimal algorithms [3, 6, 7] minimize the number of ...

Minimization methods for training feed-forward networks with Backpropagation are compared. Feedforward network training is a special case of functional minimization, where no explicit model of the data is assumed. Therefore due to the high dimensionality of the data, linearization of the training problem through use of orthogonal basis functions is not desirable. The focus is functional minimiz...

This paper proposes a new Levenberg-Marquardt algorithm that is accelerated by adjusting a Jacobian matrix and a quasi-Hessian matrix. The proposed method partitions the Jacobian matrix into block matrices and employs the inverse of a partitioned matrix to find the inverse of the quasi-Hessian matrix. Our method can avoid expensive operations and save memory in calculating the inverse of the qu...

This paper presents a novel training algorithm for fuzzy inference systems. The algorithm combines the Levenberg-Marquardt algorithm with variable structure systems approach. The combination is performed by expressing the parameter update rule in continuous time and application of sliding control method to the gradient based training procedure. In this paper, it is discussed that a fuzzy infere...

The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorithm, Hybrid Ant Bee Colony (HABC) algorithm and ...

The ability to model the time varying dynamics of an unmanned rotorcraft is an important aspect in the development of adaptive flight controller. This paper presents a recursive Gauss-Newton based training algorithm to model the attitude dynamics of a small scale rotorcraft based unmanned aerial system using the neural network (NN) modelling approach. It focuses on selection of optimised networ...