In this paper, first the space of hyperbolic tangent functions is introduced and then the universal approximator property of this space is proved. In fact, by using this space, any nonlinear continuous function can be uniformly approximated with any degree of accuracy. Also, as an application, this space of functions is utilized to design feedback control for a nonlinear dynamical system.

A penalty framework for globally solving mixed-integer nonlinear programming problems is presented. Both integrality constraints and nonlinear constraints are handled separately by hyperbolic tangent penalty functions. The preliminary numerical experiments show that the proposed penalty approach is effective and the hyperbolic tangent penalties compete with other popular penalties.

1. Notations and Assumptions 2 2. Error calculus 2 2.1. Ulp calculus 2 2.2. Relative error analysis 4 2.3. Generic error of addition/subtraction 4 2.4. Generic error of multiplication 5 2.5. Generic error of inverse 5 2.6. Generic error of division 6 2.7. Generic error of square root 7 2.8. Generic error of the exponential 7 2.9. Generic error of the logarithm 8 2.10. Ulp calculus vs relative e...

This paper discusses the propagation of signals in generic densely connected multilayered feedforward neural networks. It is concluded that the dense connecting combined with the hyperbolic tangent activation functions of the neurons may cause a highly random, spurious generalization, that decreases the overall performance and reliability of a neural network and can be mistaken with overfitting...

First, a process of building of the neural network for events forecasting is presented, that is the selection of networks’ architecture and parameters. Next, the effect of adding data calibrated by nonlinear input function to input data calibrated linearly is described. The nonlinear input function hyperbolic tangent was accepted. Hyperbolic tangent sigmoid transfer function and log sigmoid tra...

1. Error calculus 2 1.1. Ulp calculus 2 1.2. Relative error analysis 4 1.3. Generic error of addition/subtraction 4 1.4. Generic error of multiplication 5 1.5. Generic error of inverse 5 1.6. Generic error of division 6 1.7. Generic error of square root 7 1.8. Generic error of the exponential 7 1.9. Generic error of the logarithm 8 1.10. Ulp calculus vs relative error 9 2. Low level functions 9...

The contrast of an image is a feature which determines how image looks better visually. In this paper, we are analysing the capability of activation functions for contrast enhancement. Activation functions are classically used in neural network. In this paper, Activation function creates a mask which is operated on the image on pixel by pixel basis. On the basis of activation function the pixel...

In this paper, a new segmentation method using hyperbolic tangent fuzzy cmeans (MHTFCM) algorithm for medical image segmentation. The proposed method uses two hyperbolic tangent functions for clustering of images. The performance of the proposed algorithm is tested on OASIS-MRI image dataset. The performance is tested in terms of score, number of iterations (NI) and execution time (TM) under di...

Neural networks have a wide range of applications in analog and digital signal processing Nonlinear activation function is one of the main building blocks of artificial neural networks. Hyperbolic tangent and sigmoid are the most used nonlinear activation functions of NN.This project proposes a knowledge-based neural network (KBNN) modeling approach with new hyperbolic tangent function . The KB...