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EMOTION-I Model: A Biologically-Based Theoretical Framework for Deriving Emotional Context of Sensation in Autonomous Control Systems

David Tam
2007 Open Cybernetics and Systemics Journal  
It uses a probabilistic feedforward and feedback neural network with multiple adaptable gains, self-adaptive learning rate and modifiable connection weights to produce a self-organizing, selfadaptive system  ...  It is based on a biological framework for autonomous systems with minimal assumptions on the system or what emotion is.  ...  This multiple-gain adaptive control system provides the essential mechanism for learning in neural network.  ... 
doi:10.2174/1874110x00701010028 fatcat:3ffgaztizbdmrbyyhg6qsipxya

2020 Index IEEE Transactions on Fuzzy Systems Vol. 28

2020 IEEE transactions on fuzzy systems  
., An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS; TFUZZ June 2020 1062-1072 Weinstein, A., see Veloz, A., TFUZZ Jan. 2020 100-111  ...  ., +, TFUZZ Oct. 2020 2320-2334 Adaptive Fuzzy Output-Feedback Control for Switched Nonlinear Systems With Stable and Unstable Unmodeled Dynamics.  ...  ., +, TFUZZ Oct. 2020 2320-2334 Adaptive Fuzzy Output-Feedback Control for Switched Nonlinear Systems With Stable and Unstable Unmodeled Dynamics.  ... 
doi:10.1109/tfuzz.2020.3048828 fatcat:vml5fun6szcqbhpceebk3xfg2u

2021 Index IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 51

2021 IEEE Transactions on Systems, Man & Cybernetics. Systems  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TSMC Nov. 2021 7051-7062 Stable Adaptive Controller Based on Generalized Regression Neural Networks and Sliding Mode Control for a Class of Nonlinear Time-Varying Systems.  ...  ., +, TSMC Feb. 2021 943-953 Stable Adaptive Controller Based on Generalized Regression Neural Networks and Sliding Mode Control for a Class of Nonlinear Time-Varying Systems.  ... 
doi:10.1109/tsmc.2021.3136054 fatcat:b5hcsfwjw5hllpenqmaq6wpke4

Stable Adaptive Control Using New Critic Designs [article]

Paul J. Werbos
1998 arXiv   pre-print
It also offers nonlinear and neural extensions for optimal control, with empirically supported links to what is seen in the brain.  ...  Classical adaptive control proves total-system stability for control of linear plants, but only for plants meeting very restrictive assumptions.  ...  A Pathway to Stable Universal Adaptive Control and Nonlinear Control The goal of this paper is to lay the groundwork for a number of possible research directions.  ... 
arXiv:adap-org/9810001v1 fatcat:37eo7vxpt5a4veh7wv4ch6lpmy

Wavelet Fuzzy Brain Emotional Learning Control System Design for MIMO Uncertain Nonlinear Systems

Jing Zhao, Chih-Min Lin, Fei Chao
2019 Frontiers in Neuroscience  
A wavelet fuzzy brain emotional learning controller (WFBELC) model is proposed, which is comprises the benefit of wavelet function, fuzzy theory and brain emotional neural network.  ...  When it is used as the main tracking controller for a MIMO uncertain nonlinear systems, the performances of the system, such as the approximation ability, the learning performance and the convergence rate  ...  AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.  ... 
doi:10.3389/fnins.2018.00918 pmid:30662392 pmcid:PMC6328470 fatcat:pf2g3jar6bgxlcbiwo7oagso34

Review of Neural Network Algorithm and Its Application in Reactive Distillation

Huihui Wang, Ruyang Mo
2021 Asian Journal of Chemical Sciences  
Instead, neural network algorithms must be used.  ...  predict or optimize the performance of complex systems.  ...  As we all know, neural networks have flexible function approximation capabilities. The application of RBF networks in nonlinear systems has been widely discussed.  ... 
doi:10.9734/ajocs/2021/v9i319073 fatcat:xedwvxgxuvgs7cqj62ndqb7ln4

Computational intelligence in control

António E. Ruano, Shuzhi Sam Ge, Thierry Marie Guerra, Frank L. Lewis, Jose C. Principe, Matjaž Colnarič
2014 Annual Reviews in Control  
It focuses on four topics within the Computational intelligence area: neural network control, fuzzy control, reinforcement learning and brain machine interfaces.  ...  Within these topics the challenges and the relevant theoretical contributions are highlighted, as well as expected future directions are pointed out.  ...  It has been successfully applied to system control with its universal approximation and adaptation capabilities.  ... 
doi:10.1016/j.arcontrol.2014.09.006 fatcat:uo2mqqanz5f55kuo25kfjpobka

2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32

2021 IEEE Transactions on Neural Networks and Learning Systems  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TNNLS Jan. 2021 63-76 Neural Learning-Based Fixed-Time Consensus Tracking Control for Nonlinear Multiagent Systems With Directed Communication Networks.  ...  Adaptive Neural Control for a Class of Nonlinear Multiagent Systems.  ... 
doi:10.1109/tnnls.2021.3134132 fatcat:2e7comcq2fhrziselptjubwjme

Prediction of Chaotic Time Series Based on BEN-AGA Model

LiYun Su, Fan Yang, Nishant Malik
2021 Complexity  
Aiming at the prediction problem of chaotic time series, this paper proposes a brain emotional network combined with an adaptive genetic algorithm (BEN-AGA) model to predict chaotic time series.  ...  The brain emotional network model has stronger nonlinear calculation ability and generalization ability.  ...  A neural network is a powerful tool for nonlinear time series forecasting. It has the advantages of good nonlinear operation ability and high prediction accuracy. For example, Shen et al.  ... 
doi:10.1155/2021/6656958 fatcat:wdmetkibvnfi7kazozvw4ffg5a

Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Fei Chao, Dajun Zhou, Chih-Min Lin, Longzhi Yang, Changle Zhou, Changjing Shang
2019 IEEE Transactions on Cybernetics  
The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final  ...  That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing  ...  The main contributions of this paper are two-fold: 1) a new brain emotional neural network integrating a T2FIS for great nonlinear learning abilities and 2) a neural-network-based robotic controller built  ... 
doi:10.1109/tcyb.2019.2919128 pmid:31283516 fatcat:zxmdzlihhfhs3mn2lwpz2cmgbm

Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition

Yucel Cimtay, Erhan Ekmekcioglu
2020 Sensors  
This method yields a mean cross-subject accuracy of 86.56% and 78.34% on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED) for two and three emotion classes, respectively.  ...  ) for two emotion classes.  ...  Acknowledgments: We would like to thank the creators of DEAP and SEED datasets for openly sharing them with us and the wider research community.  ... 
doi:10.3390/s20072034 pmid:32260445 fatcat:su3mawykrfeeljxwocll7tph5q

Neurocontrol and fuzzy logic: Connections and designs

Paul J. Werbos
1992 International Journal of Approximate Reasoning  
Artificial neural networks (ANNs) and fuzzy logic are complementary technologies.  ...  ANNs offer universal approximation theorems, pedagogical advantages, very high throughput hardware, and links to neurophysiology.  ...  Neural adaptive control does what conventional adaptive control does, but it uses neural networks for the sake of nonlinearity and robustness; for example, an ANN may learn how to track an external  ... 
doi:10.1016/0888-613x(92)90017-t fatcat:ebkzw6hoejeldhginfnvtxz2pi

Artificial Intelligence in Civil Engineering

Pengzhen Lu, Shengyong Chen, Yujun Zheng
2012 Mathematical Problems in Engineering  
This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks  ...  Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives  ...  Benchmark and Narasimhan 47 presented a direct adaptive control scheme for the active control of the nonlinear highway bridge benchmark.  ... 
doi:10.1155/2012/145974 fatcat:asd3grpoabf5xn6tdpctxkxtwy

Speech Emotion Recognition using Deep Learning Techniques: A Review

Ruhul Amin Khalil, Edward Jones, Mohammad Inayatullah Babar, Tariqullah Jan, Mohammad Haseeb Zafar, Thamer Alhussain
2019 IEEE Access  
INDEX TERMS Speech emotion recognition, deep learning, deep neural network, deep Boltzmann machine, recurrent neural network, deep belief network, convolutional neural network.  ...  This paper presents an overview of Deep Learning techniques and discusses some recent literature where these methods are utilized for speech-based emotion recognition.  ...  The authors therefore, acknowledge with thanks DSR for technical and financial support.  ... 
doi:10.1109/access.2019.2936124 fatcat:rkj6j3defrhkhohtezarizwlbi

Machine Learning-based Modeling and Prediction of the Intrinsic Relationship between Human Emotion and Music

Jun Su, Peng Zhou
2022 ACM Transactions on Applied Perception  
The neural networks have strong internal fittability but are associated with a significant overfitting issue.  ...  This study found that nonlinear methods are more robust and predictable but considerably time-consuming than linear approaches.  ...  ACKNOWLEDGMENTS This work was supported by the ShuDi Music Institute of Chengdu Normal University and the Key Projects of Chengdu Normal University (No. CS19SA06).  ... 
doi:10.1145/3534966 fatcat:jwbl3rbp2jhdjixtdyayecgcwy
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