Supervised adaptive resonance theory neural network for modelling dynamic systems

Pham, Duc Truong; Sukkar, M.F.

doi:10.1109/icsmc.1995.538157

Cited by 3 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And in the 1940s, people have more and more fully understanding on the human brain structure, composition and the most basic unit of work with the breakthrough of neuroanatomy, neurophysiology and neuronal electrophysiological research. Based on the understanding, from the mathematical and physical methods perspective it abstracts human brain networks and establishes a simplified model, which is called artificial neural network, neural network [9] for short.…”

Section: Introductionmentioning

confidence: 99%

A rapid path planning adaptive optimization algorithm based on fuzzy neural network for multi-robot systems

Zhang

Shi

Wei

2013

International Conference on Cyberspace Technology (CCT 2013)

View full text Add to dashboard Cite

A rapid path planning adaptive optimization algorithm based on fuzzy neural network is proposed for mobile robot in a static unknown environment. In order to solve the limitations of the subjective experience in the fuzzy control and design problems of path planning, conventional neural network is given fuzzy input signals and fuzzy weights by using the membership function and the error cost function of fuzzy control theory. Neural network is constructed from fuzzy rules to accelerate the convergence of FNN, so that the collision-free motion is achieved by combining with the formation control method for mobile robot. Finally, the experiment results show that the robot path planning based on FNN algorithm is faster than other algorithms to finish the task as well as a better controllability and adaptability by the platform of Player/Stage.

show abstract

Section: Introductionmentioning

confidence: 99%

A rapid path planning adaptive optimization algorithm based on fuzzy neural network for multi-robot systems

Zhang

Shi

Wei

2013

International Conference on Cyberspace Technology (CCT 2013)

View full text Add to dashboard Cite

show abstract

A predictor based on adaptive resonance theory

Pham

Sukkar

1998

Artificial Intelligence in Engineering

View full text Add to dashboard Cite

Analysis and Comparison of the Structure and Performance of Local Neural Networks

Cong

2021

2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)

View full text Add to dashboard Cite

The paper synthesizes the local neural networks. Network structures and their activation functions of three local networks CMAC, B-spline, RBF that are often used to approach functions are analyzed and compared in detail. The network structure of ART-2 is also discussed. Based on the fuzzy system of these local networks, the paper depicts their fuzzy structures and performances. The study and analysis in the paper are useful to instruct to select and design the local neural networks.

show abstract

Supervised adaptive resonance theory neural network for modelling dynamic systems

Cited by 3 publications

References 7 publications

A rapid path planning adaptive optimization algorithm based on fuzzy neural network for multi-robot systems

A rapid path planning adaptive optimization algorithm based on fuzzy neural network for multi-robot systems

A predictor based on adaptive resonance theory

Analysis and Comparison of the Structure and Performance of Local Neural Networks

Contact Info

Product

Resources

About