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Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
Transfer Function to State Space01:23

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...

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Related Experiment Video

Updated: Jul 7, 2026

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

Comments on "discrete time neural network synthesis using input and output functions".

V Kecman1

  • 1Dept. of Mech. Eng., Auckland Univ.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

This paper critiques the mathematical underpinnings of discrete time neural networks synthesis using input and output functions (DTNNSuIOF). The authors raise objections concerning the fundamental mathematical basis of the DTNNSuIOF methodology.

Related Experiment Videos

Last Updated: Jul 7, 2026

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Mathematics

Background:

  • The paper addresses the synthesis of discrete time neural networks (DTNNs).
  • It specifically focuses on a method termed "discrete time neural networks synthesis using input and output functions" (DTNNSuIOF).
  • This work builds upon prior research by Novakovic (1996).

Purpose of the Study:

  • To critically evaluate the mathematical foundations of the DTNNSuIOF method.
  • To present objections and comments regarding the core mathematical principles of the proposed synthesis algorithm.

Main Methods:

  • The study involves a theoretical analysis of mathematical foundations.
  • It focuses on identifying and articulating objections to the DTNNSuIOF design algorithm.

Main Results:

  • The paper identifies significant issues with the mathematical basis of DTNNSuIOF.
  • Specific objections are raised concerning the core algorithms and their mathematical validity.

Conclusions:

  • The mathematical foundations of the proposed DTNNSuIOF method are found to be questionable.
  • Further scrutiny of the mathematical underpinnings is warranted before widespread adoption.