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Related Concept Videos

Signal Flow Graphs01:18

Signal Flow Graphs

Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
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.
SFG Algebra01:16

SFG Algebra

In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
Circuit Terminology01:14

Circuit Terminology

An electrical network is a system composed of interconnected elements, such as resistors, capacitors, inductors, and voltage or current sources. Unlike a circuit, an electrical network does not necessarily form a closed path. In other words, while all circuits can be considered networks due to their interconnected nature, not every network qualifies as a circuit.
A circuit, on the other hand, is also an interconnected system of electrical elements but must contain one or more closed paths.
Graphs of Functions01:30

Graphs of Functions

Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
Block Diagram Reduction01:22

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...

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Related Experiment Video

Updated: Jun 24, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Symbolic function network.

George S Eskander1, Amir F Atiya

  • 1Department of Computer Engineering, Baha Private College of Science, Al Baha, Saudi Arabia. georgeeskander@yahoo.com

Neural Networks : the Official Journal of the International Neural Network Society
|April 4, 2009
PubMed
Summary

Symbolic Function Networks (SFN) use elementary functions to build models for regression. This novel approach offers a tree-based structure and greedy search algorithms for effective function synthesis and data fitting.

Area of Science:

  • Computational intelligence
  • Machine learning
  • Symbolic regression

Background:

  • Traditional regression models often struggle with complex, nonlinear relationships.
  • Synthesizing functions from basic mathematical operations is a challenging problem in machine learning.

Purpose of the Study:

  • To introduce a novel model, the Symbolic Function Network (SFN), for function synthesis in regression tasks.
  • To develop new algorithms for constructing SFNs efficiently.

Main Methods:

  • Utilizing elementary functions (powers, exponential, logarithm) as building blocks.
  • Employing a tree-based network architecture where nodes represent operations or functions.
  • Implementing forward greedy search, backward greedy search, and steepest descent algorithms for tree construction.

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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Related Experiment Videos

Last Updated: Jun 24, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Main Results:

  • The Symbolic Function Network (SFN) effectively synthesizes functions that fit training data in a regression framework.
  • Demonstrated good performance across various test examples, showcasing the model's capability.

Conclusions:

  • Symbolic Function Networks provide a flexible and powerful approach to symbolic regression.
  • The proposed algorithms enable efficient construction of SFNs for complex function approximation.