Search results for "Error function"
showing 10 items of 11 documents
Space‐vector state dynamic model of SynRM considering self‐ and cross‐saturation and related parameter identification
2020
This study proposes a state formulation of the space-vector dynamic model of the Synchronous Reluctance Motor (SynRM) considering both saturation and cross-saturation effects. The proposed model adopts the stator currents as state variables and has been theoretically developed in both the rotor and stator reference frames. The proposed magnetic model is based on a flux versus current approach and relies on the knowledge of 11 parameters. Starting from the definition of a suitable co-energy variation function, new flux versus current functions have been initially developed, based on the hyperbolic functions and, consequently, the static and dynamic inductance versus current functions have be…
Classical Training Methods
2006
This chapter reviews classical training methods for multilayer neural networks. These methods are widely used for classification and function modelling tasks. Nevertheless, they show a number of flaws or drawbacks that should be addressed in the development of such systems. They work by searching the minimum of an error function which defines the optimal behaviour of the neural network. Different standard problems are used to show the capabilities of these models; in particular, we have benchmarked the algorithms in a nonlinear classification problem and in three function modelling problems.
Fast Convergence of Neural Networks by Application of a New Min-Max Algorithm
1992
Abstract The paper presents a new application of the min-max method, an original algorithm previously successfully applied in other areas and based on a combination of the quasi-Newton and steepest descent methods in order to find the weights minimising the error function of a feed forward neural networks. Preliminary results, obtained by applying the proposed method to a simple 2-2-1 architecture on small Boolean learning problems, are very promising.
A New Min-Max Optimisation Approach for Fast Learning Convergence of Feed-Forward Neural Networks
1993
One of the most critical aspect for a wide use of neural networks to real world problems is related to the learning process which is known to be computational expensive and time consuming.
Estimation of the elastic parameters of human liver biomechanical models by means of medical images and evolutionary computation.
2013
This paper presents a method to computationally estimate the elastic parameters of two biomechanical models proposed for the human liver. The method is aimed at avoiding the invasive measurement of its mechanical response. The chosen models are a second order Mooney–Rivlin model and an Ogden model. A novel error function, the geometric similarity function (GSF), is formulated using similarity coefficients widely applied in the field of medical imaging (Jaccard coefficient and Hausdorff coefficient). This function is used to compare two 3D images. One of them corresponds to a reference deformation carried out over a finite element (FE) mesh of a human liver from a computer tomography image, …
Two-stage adaptive designs with correlated test statistics.
2005
When performing a trial using an adaptive sequential design, it is usually assumed that the data for each stage come from different units; for example, patients. However, sometimes it is not possible to satisfy this condition or to check whether it is satisfied. In these cases, the test statistics and p-values of each stage may be dependent. In this paper we investigate the type I error of two-stage adaptive designs when the test statistics from the stages are assumed to be bivariate normal. Analytical considerations are performed under the restriction that the conditional error function is constant in the continuation region. We show that the decisions can become conservative as well as an…
Numerical study on the limit of quasi-static approximation for plasmonic nanosphere
2019
Plasmonic nanospheres are often employed as resonant substrates in many nanophotonic applications, like in enhanced spectroscopy, near-field microscopy, photovoltaics, and sensing. Accurate calculation and tuning of optical responses of such nanospheres are essential to achieve optimal performance. Mie theory is widely used to calculate optical properties of spherical particles. Although, an approximated version of Mie approach, the quasi-static approximation (QSA) can also be used to determine the very same properties of those spheres with a lot simpler formulations. In this work, we report our numerical study on the limit and accuracy of QSA with respect to the rigorous Mie approach. We c…
Periodic orbits of single neuron models with internal decay rate 0 < β ≤ 1
2013
In this paper we consider a discrete dynamical system x n+1=βx n – g(x n ), n=0,1,..., arising as a discrete-time network of a single neuron, where 0 < β ≤ 1 is an internal decay rate, g is a signal function. A great deal of work has been done when the signal function is a sigmoid function. However, a signal function of McCulloch-Pitts nonlinearity described with a piecewise constant function is also useful in the modelling of neural networks. We investigate a more complicated step signal function (function that is similar to the sigmoid function) and we will prove some results about the periodicity of solutions of the considered difference equation. These results show the complexity of …
Riccati equation-based generalization of Dawson's integral function
2007
A new generalization of Dawson's integral function based on the link between a Riccati nonlinear differential equation and a second-order ordinary differential equation is reported. The MacLaurin expansion of this generalized function is built up and to this end an explicit formula for a generic cofactor of a triangular matrix is deduced.
A Space-Vector State Dynamic Model of the Synchronous Reluctance Motor Including Self and Cross-Saturation Effects and its Parameters Estimation
2018
This paper proposes a space-vector dynamic model of the Synchronous Reluctance Motor (SynRM) including both self-saturation and cross-saturation effects and selecting as state variables the stator currents. The proposed dynamic model is based on an original function between the stator flux and stator current components, and relies on 8 coefficients (fewer than other models in the scientific literature), presenting an interesting physical interpretation. Starting from this approach, both the static and dynamic inductances expressions of the model have been analytically developed, so that the reciprocity conditions for the cross saturation is satisfied. This paper presents also a technique fo…