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Related Concept Videos

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.
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Classification of Systems-I01:26

Classification of Systems-I

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Normal and Tangetial Components: Problem Solving01:24

Normal and Tangetial Components: Problem Solving

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Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

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Group Design02:01

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Related Experiment Video

Updated: May 31, 2026

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Multi Groups Cooperation based Symbiotic Evolution for TSK-type Neuro-Fuzzy Systems Design.

Yi-Chang Cheng1, Yung-Chi Hsu, Sheng-Fuu Lin

  • 1Department of Electrical and Control Engineering, National Chiao-Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.

Expert Systems with Applications
|June 29, 2011
PubMed
Summary
This summary is machine-generated.

A novel neuro-fuzzy system, TSK-type neuro-fuzzy system with multi groups cooperation based symbiotic evolution (TNFS-MGCSE), enhances fuzzy modeling. This method uses cooperative evolution for improved rule generation in time series forecasting.

Related Experiment Videos

Last Updated: May 31, 2026

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Area of Science:

  • Computational Intelligence
  • Machine Learning
  • Time Series Analysis

Background:

  • Traditional genetic algorithms (GAs) represent rules as chromosomes.
  • Symbiotic evolution, a variant of GAs, also uses chromosomes for fuzzy model rules.
  • Existing symbiotic evolution methods lack mechanisms for enhanced cooperation.

Purpose of the Study:

  • To propose a novel TSK-type neuro-fuzzy system (TNFS-MGCSE) integrating multi-group cooperation.
  • To enhance fuzzy rule generation through a cooperative evolution strategy.
  • To evaluate the system's performance in complex time series forecasting tasks.

Main Methods:

  • Development of the TSK-type neuro-fuzzy system with multi-group cooperation based symbiotic evolution (TNFS-MGCSE).
  • Division of the population into multiple cooperating groups, each representing fuzzy rules.
  • Implementation of a cooperation-based crossover strategy (CCS) for improved chromosome generation.

Main Results:

  • The TNFS-MGCSE demonstrated excellent performance in numerical simulations.
  • The proposed system outperformed existing models in Mackey-Glass chaotic time series forecasting.
  • The TNFS-MGCSE achieved superior results in sunspot number forecasting.

Conclusions:

  • The TNFS-MGCSE effectively enhances fuzzy rule generation through cooperative evolution.
  • The proposed method offers a significant improvement over traditional symbiotic evolution and other existing models.
  • TNFS-MGCSE shows strong potential for accurate time series forecasting applications.