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...

In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where sigmoid-function-based weighting strategy developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive takes into account both distances from global best position and its personal position, thereby having distinguishing feature of enhancing convergence rate. Inspired by activatio...

An experiment using the phenomenon of percolation has been conducted to demonstrate the implementation of neural functionality (summing and sigmoid transfer). A simple analog approximation to digital percolation is implemented. The device consists of a piece of amorphous silicon with stochastic bit-stream optical inputs, in which a current percolating from one end to the other defines the neuro...

The first realization of a biomolecular OR gate function with double-sigmoid response (sigmoid in both inputs) is reported. Two chemical inputs activate the enzymatic gate processes, resulting in the output signal: chromogen oxidation, which occurs when either one of the inputs or both are present (corresponding to the OR binary function), and can be optically detected. High-quality gate functi...

This paper presents a Gaussian noise filter that uses a sigmoid shaped membership function to model image information in the spatial domain. This function acts as a tunable smoothing intensification operator. With a proper choice of two sigmoid parameters ‘t’ and ‘a’, the filter strength can be tuned for removal of Gaussian noise in intensity images. An image information measure, Total Compatib...

This work contributes to the development of a new data-driven method (D-DM) feedforward neural networks (FNNs) learning. was proposed recently as way improving randomized learning FNNs by adjusting network parameters target function fluctuations. The employs logistic sigmoid activation functions for hidden nodes. In this study, we introduce other functions, such bipolar sigmoid, sine function, ...

In this study, we present a continuous-valued logical approach to generalize the sigmoid function. Our starting point is kappa function, which known as unary operator in logic. First extend function (a,b) interval, and then interpret generalized limit of extended when b tend negative positive infinity, respectively. Since induced by an additive generator strict t-norm or t-conorm, dependent. Ba...