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- 저자 : Saeid Nahavandi (검색결과 5 건)
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Network-based synchronization of T–S fuzzy chaotic systems with asynchronous samplings
Lee, Tae H.,Lim, Chee Peng,Nahavandi, Saeid,Park, Ju H. Elsevier 2018 Journal of the Franklin Institute Vol.355 No.13
<P><B>Abstract</B></P> <P>In this paper, we study the problem of network-based synchronization of chaotic systems in Takagi–Sugeno (T–S) fuzzy form, in which the master and slave fuzzy chaotic systems are connected with a continuous-time controller through a network. In all communication channels, asynchronous samplings and external disturbances are considered. The asynchronously sampled state information of the master and slave systems received in the controller is treated by designing an observer for estimating the states of the master system. Then, based on the observation result, the problem of asynchronous samplings between the slave-controller and controller-slave channels is solved in two different cases. Sufficient conditions for the existence of the desired observer and controllers for each asynchronous cases are presented in the form of linear matrix inequalities. An numerical example is given to illustrate the validity and potential of the proposed new design techniques.</P>
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Delay-Dependent Functional Observer Design for Linear Systems With Unknown Time-Varying State Delays
Mohajerpoor, Reza,Shanmugam, Lakshmanan,Abdi, Hamid,Nahavandi, Saeid,Park, Ju H. IEEE 2018 IEEE transactions on cybernetics Vol.48 No.7
<P>Partial state estimation has numerous applications in practice. Nevertheless, designing delay-dependent functional observers (FOs) for systems with unknown time delays is rigorous and still an open dilemma. This paper addresses the problem for linear time-invariant systems with state time-varying delays. The delay is assumed to be bounded in an interval with a bounded derivative. A sliding mode FO structure that is robust against the delay uncertainties is established to this aim. The structure employs an auxiliary delay function that can be defined based on the existing knowledge on the actual delay values. Delay-dependent <I>sufficient</I> conditions for the stability of the observer are obtained using the Lyapunov Krasovskii approach, and are expressed in terms of a linear matrix inequality and two rank conditions. The <I>delay-free</I> observer structure is additionally studied and the <I>necessary and sufficient</I> conditions for its stability are obtained. Two descriptive numerical examples and simulation results demonstrate the design procedure and emphasize the effectiveness of the proposed observer design algorithm.</P>
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Comparing the Performance of Feature Selection Algorithms for Wart Treatment Selection
Roohallah Alizadehsani,Moloud Abdar,Seyed Mohammad Jafar Jalali,Mohamad Roshanzamir,Abbas Khosravi,Saeid Nahavandi 한국정보기술학회 2019 Proceedings of The International Workshop on Futur Vol.2018 No.1
Wart is a meat gland that grows on the hands, feet and other parts of the body. The best wart treatment method is still unknown to physicians. Thus, they select the treatment method randomly and change it if unsuccessful. This trial and error approach has multiple drawbacks including but not limited to prolonged treatment and increased cost. To address these issues, this study proposes a machine learning pipeline for finding the best wart treatment method. Six well-known feature selection algorithms are applied in the process of model development. Linear support vector machine (SVM), LIBSVM, and random forest algorithms are implemented as classification methods. Obtained results indicate that information gain is the best feature selection method. Also it is observed that LIBSVM achieves the highest accuracy rates for both Cryotherapy (91.11±6.67%) and Immunotherapy (88.89±6.33%) datasets.
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Mitra Arman,Kiana Pirian,Mostafa Alinaghizadeh,Fatemeh Khosheghbal,Reza Nahavandi,Saeid Tamadoni Jahromi 경희대학교 융합한의과학연구소 2022 Oriental Pharmacy and Experimental Medicine Vol.22 No.4
Nowadays, the essential oil has received a special position for the treatment of diseases. Although Satureja rechingeri Jamzad is an endemic species of Iran, unfortunately few studies have been conducted on its biological properties. In this study, along with the analysis of the compounds of Satureja rechingeri essential oil, cytotoxic, antioxidant and antibacterial properties of the essential oil of this species were investigated. The compounds of prepared essential oil were analyzed by GC-FID and GC–MS using Clevenger. Disc diffusion and MTT methods were used to determine the antibacterial activity and cytotoxicity of the essential oil, respectively. The antioxidant activity of the essential oil was measured by two methods of reducing power assay and DPPH free radical scavenging. p-Cymene (46.5%) was the most identified compound in the essential oil. The essential oil showed higher inhibitory effect on seven bacterial strains relative to the standard antibiotics. The studied essential oil showed significant concentration-dependent inhibitory effect on four cancer cells of Vero, SW480, MCF7 and JET3 with 50% lethal effect of 15.6, 125, 15.6 and 250 μg/mL for each line, respectively. The highest adsorption (2.6 nM) was at 500 μg/mL for reducing power assay and 50% free radical inhibition at a concentration of 375 μg/mL for DPPH antioxidant assay. In general, the essential oil of Satureja rechingeri with high antioxidant, antibacterial and anticancer activity can be used as a cheap and affordable natural product in clinical and pharmaceutical fields.
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NN-based Prediction Interval for Nonlinear Processes Controller
Mohammad Anwar Hosen,Abbas Khosravi,H. M. Dipu Kabir,Michael Johnstone,Douglas Creighton,Saeid Nahavandi,Peng Shi 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.9
Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.
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