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A New Data-Driven Control System for MEMSs Gyroscopes: Dynamics Estimation by Type-3 Fuzzy Systems

MICROMACHINES 2021年第11期12卷 1390-1390页

作者： Alattas, Khalid A. Mohammadzadeh, Ardashir Mobayen, Saleh Aly, Ayman A. Felemban, Bassem F. Vu, Mai The Univ Jeddah Dept Comp Sci & Artificial Intelligence Coll Comp Sci & Engn Jeddah 23890 Saudi Arabia Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Taif Univ Dept Mech Engn Coll Engn POB 11099 At Taif 21944 Saudi Arabia Sejong Univ Sch Intelligent Mech Engn Seoul 05006 South Korea

In this study, a novel data-driven control scheme is presented for MEMS gyroscopes (MEMS-Gs). The uncertainties are tackled by suggested type-3 fuzzy system with non-singleton fuzzification (NT3FS). Besides the dynamics uncertainties, the suggested NT3FS can also handle the input measurement errors. The rules of NT3FS are online tuned to better compensate the disturbances. By the input-output data set a data-driven scheme is designed, and a new LMI set is presented to ensure the stability. By several simulations and comparisons the superiority of the introduced control scheme is demonstrated.

关键词： fuzzy system learning algorithm MEMS gyroscopes machine learning LMI set data-driven control

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A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

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ENERGIES 2022年第5期15卷 1705-1705页

作者： Firouzi, Behnam Alattas, Khalid A. Bakouri, Mohsen Alanazi, Abdullah K. Mohammadzadeh, Ardashir Mobayen, Saleh Fekih, Afef Ozyegin Univ Mech Engn Dept Vibrat & Acoust Lab VAL TR-34794 Istanbul Turkey Univ Jeddah Coll Comp Sci & Engn Dept Comp Sci & Artificial Intelligence Jeddah 21959 Saudi Arabia Majmaah Univ Coll Appl Med Sci Dept Med Equipment Technol Majmaah 11952 Saudi Arabia Fezzan Univ Dept Phys Coll Arts Traghen 71340 Libya Taif Univ Fac Sci Dept Chem POB 11099 At Taif 21944 Saudi Arabia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Sch Engn & Technol Da Nang 550000 Vietnam Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA

This paper proposes a type-2 fuzzy controller for floating tension-leg platforms in wind turbines. Its main objective is to stabilize and control offshore floating wind turbines exposed to oscillating motions. The proposed approach assumes that the dynamics of all units are completely unknown. The latter are approximated using the proposed Sugeno-based type-2 fuzzy approach. A nonlinear Kalman-based algorithm is developed for parameter optimization, and linear matrix inequalities are derived to analyze the system's stability. For the fuzzy system, both rules and membership functions are optimized. Additionally, in the designed approach, the estimation error of the type-2 fuzzy approach is also considered in the stability analysis. The effectiveness and performance of the proposed approach is assessed using a simulation study of a tension leg platform subject to various disturbance modes.

关键词： wind turbine intelligent control type-2 fuzzy control (T2FLC) learning algorithm tension leg platforms (TLP)

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Predictive evaluation for software testing progress via GMDH networks

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ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE 1999年第5期82卷 22-28页

作者： Shinohara, Y Dohi, T Osaki, S Hiroshima Univ Fac Engn Higashihiroshima 739 Japan

The GMDH network is a learning machine based on the principle of heuristic self-organization. In this paper, use of the GMDH network for predicting the testing progress of software products is discussed. The fundamental GMDH and the improved GMDH using the AIC as the evaluation criterion are introduced for estimating the fault-occurrence times observed in the testing of software. Finally, in a numerical example, the GMDH network, an existing software reliability growth model, and a multilayered neural network are compared from the viewpoint of predicting performance. As a result, it is shown that the GMDH network overcomes the problem of determining an adequate network size in using a multilayered neural network and, in addition, provides a more accurate measure in evaluating software reliability than other prediction models. (C) 1999 Scripta Technica.

关键词： software reliability GMDH network learning algorithm prediction evaluation

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Exergetic Performance Prediction of a Roughened Solar Air Heater Using Artificial Neural Network

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2018年第3期64卷 195-206页

作者： Ghritlahre, Harish Kumar Prasad, Radha Krishna Natl Inst Technol Dept Mech Engn Rourkela India

The exergetic performance of a roughened solar air heater has been predicted using an artificial neural network (ANN) model. The exergetic values have been derived from the data collected by conducting experiments on solar air heater using transverse wire ribs as a roughness element on the absorber plate with relative roughness heights 0.009571, 0.01400, 0.017142 and constant relative roughness pitch 10, at local weather condition of Jamshedpur (22.77 degrees N and 86.14 degrees E), India. The ANN model was structured with seven input parameters such as experimental time, solar radiation intensity, roughness size, atmospheric temperature, mean air temperature, absorber plate temperature and mass flow rate of air in input layer, and five parameters such as exergy inlet, exergy outlet, exergy efficiency, exergy destruction and improvement potential in output layer. Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Polak-Ribiere conjugate gradient (CGP) learning algorithms have been used for training the proposed model. The LM learning algorithms with 7 neurons in the hidden layer are found to be optimal on the basis of statistical error analysis. The value of R-2 for the predicted model of exergy outlet, exergy inlet, exergetic efficiency, exergy destruction and improvement potential were 0.99584, 0.99997, 0.99517, 0.99997 and 0.99983 respectively, which yields the satisfactory performance of the ANN model. The values of mean square error (MSE), the coefficient of variance (COV), the and mean relative error (MRE) are found to be very low for predicted values of exergetic performance parameters that are desirable. The statistical results exhibit that the proposed multi-layered perceptron (MLP) ANN model successfully predicts the exergetic performance of a roughened solar air heater.

关键词： solar air heater exergy analysis artificial neural network learning algorithm multi-layer perceptron

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On the use of Blackwell's approachability theorem for stochastic dynamic programming

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IFAC Proceedings Volumes 2004年第12期37卷 765-770页

作者： Hyeong Soo Chang Department of Computer Science and Engineering Sogang University Seoul Korea

We study solving large stochastic dynamic programming problems with simulation by using Blackwell’s approachability theorem to provide a rule of generating a (history-dependent) stochastic nonstationary policy from a given finite set of policies whose performance is asymptotically not worse than any policy in the set by a given error. We provide an analysis for almost sure convergence with an exponentially fast convergence rate.

关键词： Approachability theorem Stochastic dynamic programming Sampling learning algorithm

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On the Solution of Nonlinear Fuzzy Equations System by Fuzzy Neural Networks Method

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2013年第3期15卷 388-393页

作者： Jafarian, Ahmad Nia, S. Measoomy Islamic Azad Univ Urmia Branch Dept Math Orumiyeh Iran

Neural networks have already been successfully applied to model the real world problems. The current research attempts to employ architecture of artificial neural networks for approximating solution of a system of fuzzy equations. For this aim, a multi-layer fuzzified feed-forward neural network (FFNN) on the real connection weights space is used. The proposed neural network architecture is able to approximate the unknowns by using a supervised learning algorithm which is based on the gradient descent method. The given approach has been illustrated by several examples with computer simulations.

关键词： System of fuzzy equations approximate solution fuzzified feed-forward neural network cost function learning algorithm

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FINITE CONVERGENCE OF A FUZZY δ RULE FOR A FUZZY PERCEPTRON

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NEURAL NETWORK WORLD 2008年第6期18卷 459-467页

作者： Li, Long Yang, Jie Liu, Yan Wu, Wei Dalian Univ Technol Dept Appl Math Dalian 116024 Peoples R China Dalian Univ Technol Sch Elect & Informat Engn Dalian 116024 Peoples R China Dalian Polytech Univ Dept Appl Math Dalian 116034 Peoples R China

This paper considers a fuzzy perceptron that has the same topological structure as the conventional linear perceptron. A learning algorithm based on a fuzzy 6 rule is proposed for this fuzzy perceptron. The inner operations involved in the working process of this fuzzy perceptron are based on the max-min logical operations rather than conventional multiplication and summation, etc. The initial values of the network weights are fixed as 1. It is shown that each network weight is non-increasing in the training process and remains unchanged once it is less than 0.5. The learning algorithm has an advantage, as proved in this paper, that it converges in a finite number of steps if the training patterns are fuzzily separable. This result generalizes a corresponding classical result for conventional linear perceptrons. Some numerical experiments for the learning algorithm are provided to support our theoretical findings.

关键词： Fuzzy perception learning algorithm fuzzily separable finite convergence

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Evaluating strategies for environmental sustainability in a supply chain of an emerging economy

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JOURNAL OF CLEANER PRODUCTION 2020年 262卷 121389-121389页

作者： Roy, Sanjeeb Das, Miki Ali, Syed Mithun Raihan, Ahmed Shoyeb Paul, Sanjoy Kumar Kabir, Golam Bangladesh Univ Engn & Technol Dept Ind & Prod Engn Dhaka 1000 Bangladesh Univ Technol Sydney UTS Business Sch Sydney NSW Australia Univ Regina Fac Engn & Appl Sci Ind Syst Engn Regina SK S4S 0A2 Canada

Due to an increased pressure to be environmentally sustainable, many manufacturing organizations, especially from developing countries like Bangladesh, are attempting to make necessary changes in practices and supply chains. However, those attempts need to be applied strategically with the objective to be both environmentally sustainable and economically viable. This paper offers a decision-making methodology by integrating a fuzzy cognitive map (FCM) and data envelopment analysis (DEA) for evaluating strategies for environmental sustainability based on their impact on the overall supply chain network of an organization. This paper first identifies 18 generic strategies for environmental sustainability and three supply chain performance measurement (PM) factors. Afterwards, the cause-effect relationships among these strategies and PM factors are utilized to capture the complicated relationships by FCM. The extended delta rule (EDR) learning algorithm was used in association with FCM to quantify the impact of those strategies on supply chain PM factors. Finally, DEA is used to prioritize strategies using these impact values. A real-life case using a fast-moving consumer goods (FMCG) manufacturer from Bangladesh is presented to justify the applicability of the proposed methodology. The results reflect the usefulness of this methodology for evaluating strategies for environmental sustainability in a supply chain (SC), specifically in the FMCG sector of an emerging economy. Thus, other manufacturing organizations from any industry can use this methodology to evaluate strategies for environmental sustainability. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Environmental sustainability Fuzzy cognitive map Extended delta rule learning algorithm Data envelopment analysis Bangladesh Fast-moving consumer good (FMCG) chain

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An efficient feature extraction method with pseudo-Zernike moment in RBF neural network-based human face recognition system

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EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING 2003年第9期2003卷 890-901页

作者： Haddadnia, J Ahmadi, M Faez, K Tarbiat Moallem Univ Savzevar Dept Engn Khorasan 397 Iran Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Amirkabir Univ Technol Dept Elect Engn Tehran 15914 Iran

This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF) neural network with a hybrid learning algorithm (HLA) has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT) is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI) with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR) of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.

关键词： human face recognition face localization moment invariant pseudo-Zernike moment RBF neural network learning algorithm

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Fault classification based on adaptive wavelet-based neural network

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Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis 2001年第1期21卷 21-25+70页

作者： Zhao, Z. Chen, W. Lin, Y. Chen, T. Dept. of Mechatronic Engineering Guangdong Univ. of Tech. Guangzhou 510090 China

An adaptive wavelet-based neural network is proposed by combining wavelet transformation with neural network theory in this paper. The parameters of scale and dulation of the wavelet are adjusted adaptively according to signal's characteristic in the learning process, so that the feature of the signal could be extracted to a large extent. Classification of mechanical faults based on adaptive wavelet-based neural network is also researched. The result of an example of bearing fault classification demonstrates that the neural network can classify fault accurately and reliably.

关键词： Adaptive wavelet Fault classification learning algorithm Neural networks

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