Fuzzy Neural Network with Ordered Fuzzy Numbers for Life Quality Technologies

The general goal of the research in this article is to devise an artificial neural network that requires less computational power than an ordinary one for assessing overall life satisfaction—a term often referred to as quality of life (QoL). The development of the mentioned ANN was possible due to the application of fuzzy logic, especially ordered fuzzy numbers (OFN). Research on the appliance of OFN aims at different issues such as the detection of an attack on a computer network, the anticipation of server load, management of multiplexing of data transmission paths, or transmission error rate forecasting that allows the improvement of the quality of life. It occurs due to, for instance, reduced energy demand, savings through better data transmission, and the distribution of computers’ power used in the cloud. Finally, the application of OFN on single neurons of a deep ANN allows achieving a network that is able to solve the same problem as a normal network, but with a lower number of neurons. Such networks in the future may be implemented easier in small solutions, such as solutions for the Internet of Things to improve the quality of human life. This approach is unique and has no equivalent in the literature. Due to the application of OFN in an ANN, fewer requirements for network architecture were needed to solve the same problems, and as a result, there is less demand for processor power and RAM.

[1]  Salabat Khan,et al.  Unsupervised domain adaptation using fuzzy rules and stochastic hierarchical convolutional neural networks , 2023, Comput. Electr. Eng..

[2]  Taoyan Zhao,et al.  Design of an interval type-2 fuzzy neural network sliding mode robust controller for higher stability of magnetic spacecraft attitude control. , 2023, ISA transactions.

[3]  Mingwen Shao,et al.  A novel fuzzy hierarchical fusion attention convolution neural network for medical image super-resolution reconstruction , 2022, Inf. Sci..

[4]  H. Ishibuchi,et al.  Unsupervised multilayer fuzzy neural networks for image clustering , 2022, Inf. Sci..

[5]  Sami Ben Jabeur,et al.  Bankruptcy prediction using fuzzy convolutional neural networks , 2022, Research in International Business and Finance.

[6]  S. Wen,et al.  Finite-time synchronization of T-S fuzzy memristive neural networks with time delay , 2022, Fuzzy Sets Syst..

[7]  Loan T. T. Nguyen,et al.  A hierarchical fused fuzzy deep neural network with heterogeneous network embedding for recommendation , 2022, Inf. Sci..

[8]  H. Mirzaeinejad,et al.  Tire normal force estimation using artificial neural networks and fuzzy classifiers: Experimental validation , 2022, Appl. Soft Comput..

[9]  U. Kadak.  Neural network operators of fuzzy n-cell number valued functions and multidimensional fuzzy inference system , 2022, Knowl. Based Syst..

[10]  Yunliang Jiang,et al.  Zeroing neural network with fuzzy parameter for cooperative manner of multiple redundant manipulators , 2022, Expert Syst. Appl..

[11]  Baoping Jiang,et al.  State estimation and fuzzy sliding mode control of nonlinear Markovian jump systems via adaptive neural network , 2022, J. Frankl. Inst..

[12]  S. Zhong,et al.  Fixed-time synchronization criteria of fuzzy inertial neural networks via Lyapunov functions with indefinite derivatives and its application to image encryption , 2022, Fuzzy Sets Syst..

[13]  Zhen Wang,et al.  Fixed/Preassigned-time synchronization of high-dimension-valued fuzzy neural networks with time-varying delays via nonseparation approach , 2022, Knowl. Based Syst..

[14]  Ran Zhang,et al.  The Brushless DC motor control system Based on neural network fuzzy PID control of power electronics technology , 2022, Optik.

[15]  M. Ebadzadeh,et al.  MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction , 2022, Neurocomputing.

[16]  Haiwen Wang,et al.  A recurrent neural network based fuzzy sliding mode control for 4-DOF ROV movements , 2022, Ocean Engineering.

[17]  Guorui Zhu,et al.  Adaptive fuzzy neural network-aided progressive Gaussian approximate filter for GPS/INS integration navigation , 2022, Measurement.

[18]  U. Kadak.  Multivariate fuzzy neural network interpolation operators and applications to image processing , 2022, Expert Syst. Appl..

[19]  E. Lughofer,et al.  EFNN-NullUni: An evolving fuzzy neural network based on null-uninorm , 2022, Fuzzy Sets Syst..

[20]  Li Dong,et al.  Fuzzy deep wavelet neural network with hybrid learning algorithm: Application to electrical resistivity imaging inversion , 2022, Knowl. Based Syst..

[21]  Ferani E. Zulvia,et al.  The application of gradient evolution algorithm to an intuitionistic fuzzy neural network for forecasting medical cost of acute hepatitis treatment in Taiwan , 2021, Appl. Soft Comput..

[22]  Edwin Lughofer,et al.  An interpretable evolving fuzzy neural network based on self-organized direction-aware data partitioning and fuzzy logic neurons , 2021, Appl. Soft Comput..

[23]  Sung-Kwun Oh,et al.  Hierarchical polynomial-based fuzzy neural networks driven with the aid of hybrid network architecture and ranking-based neuron selection strategies , 2021, Appl. Soft Comput..

[24]  Guolian Hou,et al.  Coordinated Control System Modeling of Ultra-Supercritical Unit based on a New Fuzzy Neural Network , 2021 .

[25]  Young Do Koo,et al.  Prediction of the internal states of a nuclear power plant containment in LOCAs using rule-dropout deep fuzzy neural networks , 2021 .

[26]  Hui Ma,et al.  Neural network-based fuzzy vibration controller for offshore platform with random time delay , 2021 .

[27]  Kaibo Shi,et al.  Almost periodic synchronization of quaternion-valued fuzzy cellular neural networks with leakage delays , 2021, Fuzzy Sets Syst..

[28]  Mien Van,et al.  Higher-order terminal sliding mode controller for fault accommodation of Lipschitz second-order nonlinear systems using fuzzy neural network , 2021, Appl. Soft Comput..

[29]  Qiuju Zhang,et al.  Design of fuzzy system-fuzzy neural network-backstepping control for complex robot system , 2021, Inf. Sci..

[30]  Paulo Vitor de Campos Souza,et al.  Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature , 2020, Appl. Soft Comput..

[31]  Lukasz Apiecionek,et al.  Limiting Energy Consumption by Decreasing Packets Retransmissions in 5G Network , 2017, Mob. Inf. Syst..

[32]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[33]  H. Ishibuchi,et al.  A learning algorithm of fuzzy neural networks with triangular fuzzy weights , 1995 .

[34]  J. Buckley,et al.  Fuzzy neural networks: a survey , 1994 .

[35]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[36]  D. Dubois,et al.  Operations on fuzzy numbers , 1978 .

[37]  Edward T. Lee,et al.  Fuzzy Neural Networks , 1975 .

[38]  R. Wai,et al.  Distributed secondary control of islanded micro-grid based on adaptive fuzzy-neural-network-inherited total-sliding-mode control technique , 2022, International Journal of Electrical Power & Energy Systems.

[39]  Sung-Kwun Oh,et al.  Fuzzy reinforced polynomial neural networks constructed with the aid of PNN architecture and fuzzy hybrid predictor based on nonlinear function , 2021, Neurocomputing.

[40]  Advances and New Developments in Fuzzy Logic and Technology , 2021, Advances in Intelligent Systems and Computing.

[41]  Eulalia Szmidt,et al.  Uncertainty and Imprecision in Decision Making and Decision Support: Cross-Fertilization, New Models and Applications , 2018 .

[42]  Dariusz Mrozek,et al.  Beyond Databases, Architectures, and Structures , 2014, Communications in Computer and Information Science.

[43]  Didier Dubois,et al.  Gradual elements in a fuzzy set , 2008, Soft Comput..

[44]  Witold Kosiński,et al.  On fuzzy number calculus , 2006 .