Modified Noise-Immune Fuzzy Neural Network for Solving the Quadratic Programming With Equality Constraint Problem

Dai, Jianhua; Luo, Liu; Xiao, Lin; Jia, Lei; Cao, Penglin; Li, J.; Krasnogor, Natalio; Wang, Yaonan

doi:10.1109/tnnls.2023.3290030

Cited by 3 publications

(1 citation statement)

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“…One kind of hybrid approach integrates the capabilities of neural networks with the noise-tolerance abilities of fuzzy logic. An example of such a hybrid technique is fuzzy neural networks (FNNs), as discussed by Dai et al [5]. The findings of studies investigating the impact of globalization on ecosystems.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Fuzzy Logic Systems for Healthcare Applications

Suzuki,

Negishi

2024

JBSHA

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Artificial Intelligence Technologies (AITs) have found application in several domains, including the area of medicine. Within this context, AITs have been leveraged for purposes such as illness diagnosis and treatment, patient monitoring, and risk evaluation. By using Artificial Intelligence Technologies (AITs), it becomes feasible to create systems that facilitate the development of intelligent models for predicting not only patients' response to therapy but also the risk of illness. Due to the intricate and uncertain nature of these domains, a multitude of scholars have developed AITs, including genetic algorithms, artificial immune systems, Artificial Neural Networks (ANN), and fuzzy logic. The integration of Fuzzy Logic Systems and ANN allows the construction of intelligent and flexible systems. ANN gain novel information by changing the connections among its distinct layers. Fuzzy logic inference frameworks provide a computational model that is grounded on fuzzy set rules, theory, and fuzzy reasoning. The amalgamation of many adaptive architectures gives rise to a "Neuro-Fuzzy" system. This research paper examines fuzzy network topologies, exploring their possible applications in the medical field. Researchers have recognized that this convergence has promise for the discovery of medical patterns.

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Section: Fuzzy Neural Networkmentioning

confidence: 99%

Fuzzy Logic Systems for Healthcare Applications

Suzuki,

Negishi

2024

JBSHA

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Discrete-time double-integral zeroing neural dynamics for time-varying equality-constrained quadratic programming with application to manipulators

Xiang,

Gong

2024

Neural Comput & Applic

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Fuzzy Neural Network for Fuzzy Quadratic Programming With Penalty Function and Mean‐Variance Markowitz Portfolio Model

Khan,

Aamir,

Ullah

et al. 2024

Applied Computational Intelligence and Soft Computing

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This research tries to integrate fuzzy neural networks with penalty function to address the quadratic programming based on the mean‐variance Markowitz portfolio model. The fuzzy quadratic programming problem with penalty function consists of the lower, central, and upper models. The models utilize fuzzy neural networks to solve the models. The proposed method has been implemented on the six leading stocks in the Pakistan Stock Exchange. The approach identifies the ideal portfolios for potential investors in the Pakistan Stock Exchange. Data of the six popular stocks trading on the stock exchange from January 2016 to October 2020 are taken into consideration. The optimizers are RMSprop, Momentum, Adadelta, Adagrad, Adam, and gradient descent, respectively. The findings of all the optimizers at all three phases (lower, central, and upper) agree on identifying the optimal investment portfolios for investors. The optimizers recommend investing in either one of the two categories. The first group recommends investing in the FFC, ARPL, and UPFL portfolios. The second group recommends LUCK, AGTL, and IGIHL. The first group tends to enhance return, variability, and risk. It is a high‐risk group. The second group aims to reduce return variability while lowering risk. It is a risk‐averse group. It is evident that all of the optimizers recommend investing in FFC, ARPL, and UPFL, with the exception of the Adam and Adadelta optimizers, which recommends investment in IGIHL, AGTL, and LUCK. RMSprop, Momentum, Adagrad, and gradient descent increase variability, risk, and returns. Adam proves the best optimizer, then RMSprop, and finally, Adagrad. Adam, Adadelta, and RMSprop are sensitive, whereas momentum and gradient descent are irresponsive to fuzzy uncertain data. The percent improvement in the objective is 0.59% and 0.18% for the proposed Adagrad and Adadelta, respectively.

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Modified Noise-Immune Fuzzy Neural Network for Solving the Quadratic Programming With Equality Constraint Problem

Cited by 3 publications

References 0 publications

Fuzzy Logic Systems for Healthcare Applications

Fuzzy Logic Systems for Healthcare Applications

Discrete-time double-integral zeroing neural dynamics for time-varying equality-constrained quadratic programming with application to manipulators

Fuzzy Neural Network for Fuzzy Quadratic Programming With Penalty Function and Mean‐Variance Markowitz Portfolio Model

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