Related Experiment Videos

Backpropagation and ordered derivatives in the time scales calculus

John Seiffertt1, Donald C Wunsch

  • 1Missouri University of Science and Technology, Rolla, MO, USA. seiffertt@ieee.org

IEEE Transactions on Neural Networks
|July 10, 2010
PubMed
Summary

This study introduces a generalized backpropagation algorithm using time scales calculus, unifying continuous and discrete neural network learning. This novel approach enhances the widely used backpropagation technique for broader applications.

Related Concept Videos

Higher Derivatives01:29

Higher Derivatives

In calculus, higher-order derivatives extend the idea of differentiation beyond the first derivative to capture successive rates of change. These derivatives provide detailed information about the behavior of functions and have important applications in both mathematics and physics. To illustrate these concepts, consider the example function\begin{equation*}f(x) = x^3 - x\end{equation*}which serves as a useful case study for exploring higher derivatives.The first derivative represents the slope...
Second Derivatives and Laplace Operator01:22

Second Derivatives and Laplace Operator

The first order operators using the del operator include the gradient, divergence and curl. Certain combinations of first order operators on a scalar or vector function yield second order expressions. Second-order expressions play a very important role in mathematics and physics. Some second order expressions include the divergence and curl of a gradient function, the divergence and curl of a curl function, and the gradient of a divergence function.
Consider a scalar function. The curl of its...
Partial Differential Equations01:21

Partial Differential Equations

A stone dropped into a still pond generates waves that propagate outward in circular patterns, creating a dynamic surface whose elevation depends on both position and time. At any given location, the water level oscillates as the wave passes, while at any fixed moment, the surface exhibits smooth, curved structures extending across space. This dual dependence requires a mathematical description that accounts for variation in multiple variables simultaneously.At a fixed point on the water...
Derivatives01:15

Derivatives

DerivativesThe concept of instantaneous rate of change is fundamental in both mathematics and physics, particularly in describing how a moving object alters its position with respect to time. This rate is captured mathematically through the derivative of a function. The derivative at a point represents the slope of the tangent line to the curve of the function at that point and quantifies how the function’s output changes per infinitesimal change in input.Derivative of the Square Root...
Derivatives of Vector Functions01:17

Derivatives of Vector Functions

A vector-valued function describes position as a function of time. For example, in Cartesian coordinates, the position of a car moving along a curved road can be written as\begin{equation*}\textbf{r}(t)=\langle x(t),y(t),z(t)\rangle\end{equation*}Secant Vector and Average Velocity:This secant vector captures the overall change in position during the interval and provides a crude estimate of the direction of motion.At time t, the car is at point P, with position r(t). After a short interval h,...
The Derivative as a Function01:26

The Derivative as a Function

A derivative quantifies how a function changes in response to variations in its input. It provides a localized rate of change, representing the slope of the tangent line to the function at any given point. When this process is applied systematically across the entire domain of the function, it yields a new function—the derivative function—which encodes the rate of change at every point. This concept is central to calculus and essential for understanding the behavior of dynamic systems in both...