Related Experiment Video

Updated: May 12, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

A new upper bound for the norm of interval matrices with application to robust stability analysis of delayed neural

Ozlem Faydasicok1, Sabri Arik

  • 1Istanbul University, Department of Mathematics, 34134 Vezneciler, Istanbul, Turkey. kozlem@istanbul.edu.tr

Neural Networks : the Official Journal of the International Neural Network Society
|April 11, 2013
PubMed

More Related Videos

Isolation and Quantification of Botulinum Neurotoxin From Complex Matrices Using the BoTest Matrix Assays12:25

Isolation and Quantification of Botulinum Neurotoxin From Complex Matrices Using the BoTest Matrix Assays

Published on: March 3, 2014

Related Concept Videos

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the key values are 3...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first column of the Routh...
Introduction to Nonlinear Inequalities01:25

Introduction to Nonlinear Inequalities

Linear and nonlinear inequalities are fundamental for analyzing variable relationships and identifying ranges satisfying specific conditions. A linear inequality involves variables raised only to the first power, resulting in a straight-line graph. This line partitions the coordinate plane into two distinct regions: one that satisfies the inequality and one that does not. Each region represents a set of solutions where the linear relationship holds true under the specified constraint.Nonlinear...
Critical Numbers and the Closed Interval Method01:21

Critical Numbers and the Closed Interval Method

Understanding the maximum and minimum values of a function is essential for analyzing its overall behavior. These values, often referred to as extrema, provide insight into how a function behaves across its domain. In mathematical terms, extrema can be either local—representing peaks and valleys within a limited region—or absolute, indicating the highest or lowest points over an entire interval.A function’s extrema occur at critical numbers, which are values in the domain where the derivative...