Approximate solutions of fuzzy optimal control problems using sigmoid-weighted neural networks

Fard, Saeed Panahian; Pourabbas, Rahim; Pouramini, Jafar

doi:10.1007/s00500-020-05534-y

Cited by 4 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This phenomenon is called error pattern recognition (EPR) in pattern recognition [31][32]. For example, using BP neural network algorithm and fuzzy neural network method are both easy to cause EPR [33] . The pattern recognition accuracy will decrease to less than 50% of the original value upon the occurrence of EPR.…”

Section: Comparative Studymentioning

confidence: 99%

Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural Network

Xie,

Zhao,

Zhu

2024

Preprint

View full text Add to dashboard Cite

To address difficult recognition of vibration signals of heavy-load robots, a new median neural network pattern recognition method based on artificial neural network was proposed. This method builds the pattern recognition network model by using the median neurons and particle swarm optimization algorithm. In other words, the fault modes of heavy-load industrial robots are recognized quickly and accurately by using median and multiplication function as the aggregate function, and combining with the particle swarm backpropagation algorithm under the premise that there’s abnormal or fault eigenvalues in the network input mode. According to the simulation analysis and case verification, the proposed median neural network method can recognize fault modes of heavy-load robots well and it still and a smaller one recognition fault rate more traditional algorithms In below the complicated severe environment with noises. It shows remarkable recognition accuracy as well as relatively high compatibility and stability. This method not only has high theoretical value in pattern recognition of heavy-duty robots, but also possesses very important engineering practice significance in fault pattern recognition of rotary machines like ventilator and gas blower.The proposed method not only has high theoretical value in pattern recognition of heavy-duty robots, but there are also important,it has practical significance in engineering and other fields fault pattern recognition of rotating machinery such as ventilators and gas blowers.

show abstract

Section: Comparative Studymentioning

confidence: 99%

Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural Network

Xie,

Zhao,

Zhu

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The system of ordinary differential equations is first order nonlinear. Recently, we have proposed a three layer feedforward sigmoidweighted neural networks for approximation problems [15,16]. In this study, we propose our method to approximate the solutions of this system of ordinary differential equations.…”

Section: Introductionmentioning

confidence: 99%

Approximate solutions of the biochemical reaction kinetics model for methane production from anaerobic digestion process of cellulose using sigmoid-weighted neural networks

Fard

Pouramini

Hafshejani

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Anaerobic digestion process is a spreading rapidly biotechnology for conversion of various organic wastes into bioenergy. This process can be modelled as a biochemical reaction kinetics. This approach is useful for analysis of the process. For this reason, efficient numerical methods are needed to solve this type of model. The main contribution of this study is to solve the biochemical reaction kinetics model for methane production from anaerobic digestion process of cellulose. This model provides a system of ordinary differential equations. For solving this system, a method is designed based on the universal approximation capability of a three layer feedforward sigmoidweighted neural networks with Adam optimization algorithm. This method is illustrated with numerical simulations. Moreover, the classical numerical methods such as Runge-Kutta method (RK45) and sigmoid neural networks are presented to show that all methods yield similar results.

show abstract

An Investigation of Approximate Solutions for Second Order Ordinary Differential Equations using Sigmoid-Weighted Neural Networks

Fard

Pouramini

2022

Int. J. Appl. Comput. Math

View full text Add to dashboard Cite

Approximate solutions of fuzzy optimal control problems using sigmoid-weighted neural networks

Cited by 4 publications

References 12 publications

Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural Network

Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural Network

Approximate solutions of the biochemical reaction kinetics model for methane production from anaerobic digestion process of cellulose using sigmoid-weighted neural networks

An Investigation of Approximate Solutions for Second Order Ordinary Differential Equations using Sigmoid-Weighted Neural Networks

Contact Info

Product

Resources

About