Neural network is one of the new soft computing methods commonly used for prediction of the thermodynamic properties of pure fluids and mixtures. In this study, we have used this soft computing method to predict the coefficients of the Antoine vapor pressure equation. Three transfer functions of tan-sigmoid (tansig), log-sigmoid (logsig), and linear were used to evaluate the performance of diff...

Special attention must be paid to an efficient approximation of the sigmoid function in implementing FPGA-based reprogrammable hardware-based artificial neural networks. Four previously published piecewise linear and one piecewise second-order approximation of the sigmoid function are compared with SIG-sigmoid, a purely combinational approximation. The approximations are compared in terms of sp...

We define a new subclass of univalent function based on Salagean differential operator and obtained the initial Taylor coefficients using the techniques of Briot-Bouquet differential subordination in association with the modified sigmoid function. Further we obtain the classical Fekete-Szego inequality results.

We consider the use of sigmoid functions for multistage detection in asynchronous code-division multiple-access (CDMA) systems. The sigmoid decision function for each stage of multistage detection is derived under the assumption that the residual noise which remains after the multiple-access interference (MAI) cancellation at a stage is Gaussian. It is suggested that the sigmoid function should...

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...

This paper shows that the performance of the Hoppeld network for solving optimization problems can be improved by using a new activation (output) function. The eeects of the activation function on the performance of the Hoppeld network are analyzed. It is shown that the sigmoid activation function in the Hoppeld network is sensitive to noise of neurons. The reason is that the sigmoid function i...

-The sigmoid fimction & very widely used as a neuron activation fimction in artificial neural networks. which makes its attributes a matter o f some interest. This paper presents some general results on the derivatives o f the sigmoid. These results relate the coefficients o f various derivatives to standard number sequences from combinatorial theory, and thus provide a standard efficient way o...