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Design and application of interval type-2 fuzzy neural network systems optimized with hybrid algorithms

INFORMATION SCIENCES 2025年 689卷

作者： Chen, Yang Liaoning Univ Technol Coll Sci Jinzhou 121000 Peoples R China

Interval type-2 fuzzy neural network systems (IT2 FNNSs) own the advantages of IT2 fuzzy logic systems (FLSs) and NN. The paper designs a kind of IT2 FNNSs for permanent magnetic drive (PMD) forecasting. For each rules of IT2 FNNSs, whose antecedents, consequents and input measurements are chosen as Gaussian IT2 membership functions (MFs). The proposed hybrid backpropagation (BP) algorithms and recursive least square (RLS) algorithms are adopted to tune all the parameters simultaneously. Simulation instances on the basis of data of permanent magnetic drive (PMD) torque and revolutions per minute (rpm) are adopted for testing the performances of proposed hybrid optimized IT2 FNNSs for forecasting. Convergence analysis illustrates that the IT2 FNNSs have excellent generalization capability compared with both singleton and non-singleton T1 FNNSs.

关键词： Type-2 fuzzy neural network systems Interval type-2 membership functions back propagation algorithms Recursive least square algorithms Convergence analysis

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Artificial intelligence without restriction surpassing human intelligence with probability one: Theoretical insight into secrets of the brain with AI twins of the brain

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NEUROCOMPUTING 2025年 619卷

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin Southeast Univ Sch Automat Nanjing Peoples R China Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing Peoples R China Mind PointEye Singapore Singapore Harvard Med Sch Beth Israel Deaconess Med Ctr Boston MA USA Natl Neurosci Inst Dept Neurol Singapore Singapore Natl Univ Singapore Duke NUS Med Sch Singapore Singapore Sun Yat Sen Univ Affiliated Hosp 1 Res Ctr Big Data & Artificial Intelligence Med Guangzhou Peoples R China Tsinghua Univ Inst AI Ind Res Beijing Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Tsinghua Univ Sch Biomed Engn Beijing Peoples R China Singapore Inst Technol Singapore Singapore Lingnan Univ Sch Data Sci Hong Kong Peoples R China Univ Birmingham Sch Comp Sci Birmingham England Singapore Natl Eye Ctr Singapore Eye Res Inst Singapore Singapore Tsinghua Univ Beijing Peoples R China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its selfcontained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundamenta

关键词： AI Twins Neuroscience Artificial Intelligence back propagation algorithms Spiking Extreme Learning Machines Cellular AI

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Design of Takagi Sugeno Kang Type Interval Type-2 Fuzzy Logic Systems Optimized with Hybrid algorithms

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2023年第2期25卷 868-879页

作者： Chen, Yang Yang, Jiaxiu Li, Chenxi Liaoning Univ Technol Coll Sci Jinzhou 121001 Peoples R China

The paper designs a type of Takagi-Sugeno-Kang (TSK) type interval type-2 fuzzy logic systems for permanent magnetic drive (PMD) coercivity and maximum energy product (MEP) forecasting. The antecedents and input measurements of interval type-2 fuzzy logic systems (IT2 FLSs) are selected as Gaussian IT2 membership function (MFs) with uncertain standard deviations. The back propagation (BP) algorithms are adopted for tuning the parameters of antecedent and input measurement. Meanwhile, the recursive least square (RLS) algorithms are adopted for tuning the parameters of consequent. Monte Carlo computer simulation examples are provided to illustrate the effective of hybrid optimized IT2 FLSs in contrast to two types of type-1 (T1) FLSs.

关键词： Interval type-2 fuzzy logic systems Recursive least square algorithms back propagation algorithms Convergence Monte Carlo simulation

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Age and Gender Classification Using backpropagation and Bagging algorithms

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Computers, Materials & Continua 2023年第2期74卷 3045-3062页

作者： Ammar Almomani Mohammed Alweshah Waleed Alomoush Mohammad Alauthman Aseel Jabai Anwar Abbass Ghufran Hamad Meral Abdalla Brij B.Gupta Research and Innovation Department Skyline University CollegeP.O.Box 1797SharjahUAE IT-department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Prince Abdullah Ben Ghazi Faculty of Information and Communication Technology Al-Balqa Applied UniversityAl-SaltJordan School of Information Technology Skyline University CollegeSharjah P.O.Box 1797United Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Department of Computer Science and Information Engineering Asia UniversityTaizhongTaiwan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia

Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security *** main aim of the research is to develop a system able to predicate and classify gender,age,and ***,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is *** and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender *** has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification *** has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.

关键词： Classify voice gender accent age bagging algorithms back propagation algorithms AI classifiers

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Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第12期43卷 2780-2787页

作者： Chen, Yang Yang, Jiaxiu Liaoning Univ Technol Coll Sci Shiying Dist Jinzhou 121001 Liaoning Peoples R China

In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with the type-1 fuzzy logic systems, simulation studies show that the proposed IT2 FLSs can obtain better abilities of generalization for fuzzy identification problems.

关键词： Fuzzy identification interval type-2 fuzzy logic systems Nagar-Bardini structure back propagation algorithms simulations

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Failure Modeling of C-130 Turbines using Artificial Neural Networks 67

Failure Modeling of C-130 Turbines using Artificial Neural N...

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67th Annual Reliability and Maintainability Symposium (RAMS)

作者： Qattan, Nizar A. Al-Bahi, Ali M. Kada, Belkacem King Abdulaziz Univ Dept Aeronaut Engn Jeddah 21589 Saudi Arabia King Abdulaziz Univ Dept Aerosp Engn Jeddah 21589 Saudi Arabia

ISBN: (纸本)9781728180175

The C-130 aircraft is one of the most widely used medium transports in the world. It operates virtually everywhere, from the arctic circle to the Sahara. Operation in desert conditions, however, presents a challenge for maintenance engineers regarding preventive maintenance scheduling. Erosion caused by sand particles drastically decreases turbine blades life. Recent studies showed that Artificial Neural Network ANN algorithms have much better capability at modeling reliability and predicting failure than conventional algorithms. In this study, more than thirty years of local operational field data were used for failure rate prediction and validation using several algorithms. These include Weibull regression modeling to establish a reference, feed-forward back-propagation ANN, and radial basis neural network algorithm. Comparison between the three methods is carried out. Results show that the failure rate predicted by both the feed-forward back-propagation artificial neural network model and radial basis neural network model are closer to actual failure data than he failure rate predicted by the Weibull model. The results also give an insight into the reliability of the engine turbine under actual operating conditions, which can be used by aircraft operators for assessing system and component failures and customizing the maintenance programs recommended by the manufacturer.

关键词： Reliability Neural Network back propagation algorithms Turbine Blades

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A Comparative Evaluation of Conventional and Computational Intelligence Techniques for Forecasting Electricity Outage

A Comparative Evaluation of Conventional and Computational I...

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Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern-Recognition-Association of South Africa (SAUPEC/RobMech/PRASA)

作者： Onaolapo, A. K. Pillay-Carpanen, R. Dorrell, D. G. Ojo, E. E. Univ Kwazulu Natal Discipline Elect Elect & Comp Engn ZA-4041 Durban South Africa Univ Witwatersrand Sch Elect & Informat Engn Johannesburg South Africa Durban Univ Technol Dept Elect Power Engn ZA-4000 Durban South Africa

ISBN: (纸本)9781665403450

Reliability of the electric grid structure for the transmission and distribution of power from the generating plants to the consumers, is an essential requirement for the reliability of electric supply. The components of the grid is exposed to weather events which cause faults to the grid's structures. There is an expectation that as climate change alters the severity and number of weather events, electricity supplies and electricity grid reliability are expected to be affected. Computational intelligence models have been proven to be excellent predictive models for electricity reliability problems in previous studies. But there has not been sufficient literature reporting their applications in weather related electricity outage forecasting problems. In this study, weather related electricity outage was forecasted using artificial neural networks (ANNs) with hack-propagation algorithm. Real -life data sets of the city of Pietermaritzburg, South Africa, was used to investigate the performance of the ANN model and was compared with a conventional model exponential smoothing (ES). The Al model gave satisfactory results as compared to the ES model. The result is a demonstration of the robustness of computation techniques.

关键词： artificial neural network back propagation algorithms exponential smoothing outage forecasting weather events

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Research on Pest and Disease Recognition algorithms Based on Convolutional Neural Network

Research on Pest and Disease Recognition Algorithms Based on...

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2019虚拟现实与智能系统国际会议

作者： Haiyan Hu Chang Su Ping Yu Department of Information Engineering Jilin Business and Technology College Information Center Changchun Eleventh High School Changchun Institute of Technology Jilin Province Science and Technology Innovation Center for Physical Simulation and Security of Water Resources and Electric Power Engineering

In order to detect crop diseases and insect pests in crop cultivation timely and accurately and to determine which kind of diseases and insect pests,this paper uses some algorithms in the field of image processing and the prevailing neural network to identify crop diseases and insect *** order to better identify diseases and insect pests,this paper uses a variety of plant diseases and insect pests for analysis and research,then expands a variety of crop diseases and insect pests on the original basis,expands the practical field by modifying model parameters and other methods,which not only saves workload,but also saves a lot of time,and can be based on the ***-time conditions to make a better pest identification algorithm model.

关键词： Convolutional Neural Network Pest and Disease Recognition algorithms back propagation algorithms

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Raman spectroscopy for rapid and inexpensive diagnosis of echinococcosis using the adaptive iteratively reweighted penalized least squares-Kennard-stone-back propagation neural network

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LASER PHYSICS LETTERS 2018年第8期15卷

作者： Zheng, Xiangxiang Lu, Guodong Du, Guoli Yue, Xiaxia Lu, Xiaoyi Tang, Jun Mo, Jiaqing Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China Xinjiang Med Univ Affiliated Hosp 1 Urumqi 830000 Peoples R China Xinjiang Univ Phys & Chem Detecting Ctr Urumqi 830046 Peoples R China

A rapid and inexpensive method of screening and diagnosis for echinococcosis is proposed for Raman spectroscopy, together with improved neural networks. We use the adaptive iteratively reweighted penalized least squares (airPLS) algorithm to deduct the fluorescence background from the Raman spectra of healthy people and echinococcosis patients. The processed data was compressed into the principal component by the PLS method, and the Kennard-stone (KS) algorithm was used to divide it into a training set and a testing set. Finally, the data was put into the back propagation (BP) neural network for modeling and prediction. The results show that the true positive rate of echinococcosis diagnosis is (94.2857 +/- 4.0721)%, the true negative rate is (95.2381 +/- 0)% and the overall accuracy rate is (94.6939 +/- 2.3269)%. The algorithm is compared with three other algorithms and it is shown that its superiority can be proved. The Raman spectroscopy combined with the airPLS-KS-BP algorithm can achieve fast and accurate diagnosis of echinococcosis.

关键词： Raman spectroscopy back propagation algorithms data processing diseases fluorescence

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Application of back propagation Neural Network algorithms on Modeling Failure of B-737 Bleed Air System Valves in Desert Conditions

Application of Back Propagation Neural Network Algorithms on...

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AEROTECH 4 - Conference on Recent Advances in Aerospace Technologies

作者： Abdelrahman, Wael G. Al-Garni, Ahmed Z. Al-Wadiee, Waheed King Fahd Univ Petr & Minerals Dept Aerosp Engn Dhahran 31261 Saudi Arabia Aviat Dept Saudi Aramco Dhahran Saudi Arabia

ISBN: (纸本)9783037855065

Accurate life prediction of aircraft engine components is very critical because it has a direct impact on aircraft safety and on operators' profits. The engine bleed air system valves have considerably high failure rates when the engines are operated in desert conditions because of sand particles erosion and blockage. In this work, an Artificial Neural Network (ANN) model for the prediction of failure rate of the most important of these valves in Boeing 737 engines is developed and validated. A previously developed feed-forward back-propagation algorithm is implemented to train the ANN. The effects of changing the number of neurons in the input layer, the number of neurons in the hidden layer, the rate of learning, and the momentum constant are investigated. The model results are validated using comparisons with actual valves failure data from a local operator in Saudi Arabia, as well as comparisons with classical Weibull model results.

关键词： reliability neural network back propagation algorithms engine valves desert conditions

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