A new Perceptron training algorithm is presented, which employs the piecewise linear activation function and the sum of squared differences error function over the entire training set. The most commonly used activation functions are continuously differentiable such as the logistic sigmoid function, the hyperbolic-tangent and the arctangent. The differentiable activation functions allow gradient...

BACKGROUND The purpose of this study was to evaluate characteristics of Modic changes in cervical kyphosis (CK) and their association with axial neck pain. METHODS Study participants included 286 asymptomatic or symptomatic patients with CK (mean age = 54.2 ± 12.2 years) who were consecutively enrolled from March 2009 to October 2015. Clinical and radiographic evaluations were performed at a ...

The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems. This is done by adding a sigmoid weight victor in the hidden layer neurons to adapt of the shape of the sigmoid function making their outputs not restricted to the sigmoid function output. Also, we in...

We propose a novel multi-party computation protocol for evaluating continuous real-valued functions with high numerical precision. Our method is based on approximations with Fourier series and uses at most two rounds of communication during the online phase. For the offline phase, we propose a trusted-dealer and honest-but-curious aided solution, respectively. We apply our algorithm to train a ...

Experimental data of different animals (e.g. cocks, pigs, cats, dogs, cattles, etc.) from recent bibliography were selected to evaluate the capability of five classical sigmoidal equations (i.e. Bertalanffy, Weibull, logistic, Gompertz, and modified Hill) to model growth. These functions were used in different reparameterized forms in order to define all growth phases and to characterize signif...

Many growing phenomena in both nature and society can be predicted with sigmoid function. The growth curve of the level of urbanization is a typical S-shaped one, and can be described by using logistic function. The logistic model implies a replacement process, and the logistic substitution suggests non-linear dynamical behaviors such as bifurcation and chaos. Using mathematical transform and n...

So far, we’ve mainly been talking about learning algorithms that model p(y|x; θ), the conditional distribution of y given x. For instance, logistic regression modeled p(y|x; θ) as hθ(x) = g(θx) where g is the sigmoid function. In these notes, we’ll talk about a different type of learning algorithm. Consider a classification problem in which we want to learn to distinguish between elephants (y =...