Neuro-fuzzy identification of an internal combustion engine

Dynamic modeling and identification of an internal combustion engine (ICE) model is presented in this paper. Initially, an analytical model of an internal combustion engine simulated within SIMULINK environment is excited by pseudorandom binary sequence (PRBS) input. This random signals input is chosen to excite the dynamic behavior of the system over a large range of frequencies. The input and output data obtained from the simulation of the analytical model is used for the identification of the system. Next, a parametric modeling of the internal combustion engine using recursive least squares (RLS) technique within an auto-regressive external input (ARX) model structure and a nonparametric modeling using neuro-fuzzy modeling (ANFIS) approach are introduced. Both parametric and nonparametric models verified using one-step-ahead (OSA) prediction, mean squares error (MSE) between actual and predicted output and correlation tests. Although both methods are capable to represent the dynamic of the system very well, it is demonstrated that ANFIS gives better prediction results than RLS in terms of mean squares error achieved between the actual and predicted signals

Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications

Aim Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification of nonlinear systems. The network considers both global and local properties, deals with imprecision present in sensory data, leading to desired precisions. In this paper, we proposed a new FWNN model nominated “Fuzzy Jump Wavelet Neural Network” (FJWNN) for identifying dynamic nonlinear-linear systems, especially in practical applications. Methods The proposed FJWNN is a fuzzy neural network model of the Takagi-Sugeno-Kang type whose consequent part of fuzzy rules is a linear combination of input regressors and dominant wavelet neurons as a sub-jump wavelet neural network. Each fuzzy rule can locally model both linear and nonlinear properties of a system. The linear relationship between the inputs and the output is learned by neurons with linear activation functions, whereas the nonlinear relationship is locally modeled by wavelet neurons. Orthogonal least square (OLS) method and genetic algorithm (GA) are respectively used to purify the wavelets for each sub-JWNN. In this paper, fuzzy rule induction improves the structure of the proposed model leading to less fuzzy rules, inputs of each fuzzy rule and model parameters. The real-world gas furnace and the real electromyographic (EMG) signal modeling problem are employed in our study. In the same vein, piecewise single variable function approximation, nonlinear dynamic system modeling, and Mackey–Glass time series prediction, ratify this method superiority. The proposed FJWNN model is compared with the state-of-the-art models based on some performance indices such as RMSE, RRSE, Rel ERR%, and VAF%. Results The proposed FJWNN model yielded the following results: RRSE (mean±std) of 10e-5±6e-5 for piecewise single-variable function approximation, RMSE (mean±std) of 2.6–4±2.6e-4 for the first nonlinear dynamic system modelling, RRSE (mean±std) of 1.59e-3±0.42e-3 for Mackey–Glass time series prediction, RMSE of 0.3421 for gas furnace modelling and VAF% (mean±std) of 98.24±0.71 for the EMG modelling of all trial signals, indicating a significant enhancement over previous methods. Conclusions The FJWNN demonstrated promising accuracy and generalization while moderating network complexity. This improvement is due to applying main useful wavelets in combination with linear regressors and using fuzzy rule induction. Compared to the state-of-the-art models, the proposed FJWNN yielded better performance and, therefore, can be considered a novel tool for nonlinear system identificationPeer ReviewedPostprint (published version

Fuzzy model identification and self learning with smooth compositions

This Paper Develops A Smooth Model Identification And Self-Learning Strategy For Dynamic Systems Taking Into Account Possible Parameter Variations And Uncertainties. We Have Tried To Solve The Problem Such That The Model Follows The Changes And Variations In The System On A Continuous And Smooth Surface. Running The Model To Adaptively Gain The Optimum Values Of The Parameters On A Smooth Surface Would Facilitate Further Improvements In The Application Of Other Derivative Based Optimization Control Algorithms Such As Mpc Or Robust Control Algorithms To Achieve A Combined Modeling-Control Scheme. Compared To The Earlier Works On The Smooth Fuzzy Modeling Structures, We Could Reach A Desired Trade-Off Between The Model Optimality And The Computational Load. The Proposed Method Has Been Evaluated On A Test Problem As Well As The Non-Linear Dynamic Of A Chemical Process.This publication was supported in part by project MINECO, TEC2017-88048-C2-2-

Parallel Computing and Neural Networks in Behavioral Modeling

Tato disertační práce se zabývá metodami modelování elektronického zařízení letadel. První část je stručným přehledem klasických metod modelování systémů a adaptivních, fuzzy a hybridních metod používaných převážně k black-box modelování. Cílem práce je vytvořit algoritmus pro identifikaci a modelování obecného systému, který může být nelineární, dynamický a velmi složitý, například co do množství rozměrů. Předpokládá se, že model má několik vstupů a výstupů. V hlavní části práce je rozebrána metoda, která patří mezi hybridní systémy, protože kombinuje fuzzy systém s parametricky definovanými pravidly a regresní neuronovou síť. Nejprve je zmíněn základní princip regresní sítě a způsob určení jejího parametru strmosti, dále se kapitola zabývá zavedením fuzzy pravidel do této sítě. Třetí část se zabývá jedním z hlavních bodů práce, paralelními výpočty. Výsledný algoritmus je navržen pro paralelní zpracování, protože výpočetní čas může být v případě složitějších modelů příliš vysoký, případně nelze výsledky získané ze sítě vyhodnotit pomocí výpočtu v jednom vlákně. V závěru práce je metoda ověřena na datech získaných z měření zmenšeného modelu letadla. Ověření je provedeno pomocí střední kvadratické odchylky a srovnáním s odpovídajícím modelem vytvořeným pomocí vícevrstvé neuronové sítě trénované zpětným šířením chyby s algoritmem Levenberg-Marquardt.This thesis is focused on methods for the aircraft equipment modeling. The first part provides a brief overview of classical system modeling approaches used for system description, identification, and modeling. Then adaptive, fuzzy and hybrid methods used mainly for black-box system modeling are introduced. Aim of the thesis is to develop an algorithm for identification and modeling of a general system, which can be nonlinear, dynamic and complex. Multiple inputs and multiple outputs of model are assumed. The main part of the thesis introduces a new method which falls into the hybrid systems. It combines fuzzy approach with parametrically defined rules and general regression neural network. Firstly, the fundamentals of simple general regression neural network and its smoothness parameter determination are presented. Secondly, the general regression neural network with the fuzzy rules is introduced. Third part of the thesis is focused on the parallel computing, one of the main objectives. The final algorithm is designed for the parallel machine, because the computational time can be significantly high and for the larger datasets, the model is not achievable when evaluated in single thread. Block diagram for parallel computing in Matlab and CUDA is provided, as well as the basic structure of CUDA source code. Finally, the method is verified on data obtained from the measurement of a miniaturized aircraft model using the antenna outside the aircraft and the probe inside the fuselage of the aircraft model. The validation of the method is done using mean squared error and compared to mean squared error of corresponding model performed using the multilayer neural network with backpropagation learning and Levenberg-Marquardt algorithm.

On the interpretation and identification of dynamic Takagi-Sugenofuzzy models

Dynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when they are identified from experimental data. It is shown that there exists a close relationship between dynamic Takagi-Sugeno fuzzy models and dynamic linearization when using affine local model structures, which suggests that a solution to the multiobjective identification problem exists. However, it is also shown that the affine local model structure is a highly sensitive parametrization when applied in transient operating regimes. Due to the multiobjective nature of the identification problem studied here, special considerations must be made during model structure selection, experiment design, and identification in order to meet both objectives. Some guidelines for experiment design are suggested and some robust nonlinear identification algorithms are studied. These include constrained and regularized identification and locally weighted identification. Their usefulness in the present context is illustrated by examples

New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use

Penguasaan kemahiran generik di kalangan graduan hospitaliti di politeknik : satu kajian berkenaan keperluan industri perhotelan, persepsi pensyarah dan pelajar

Kajian yang dijalankan ini bertujuan untuk mengenal pasti kepentingan kemahiran generik mengikut keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan persepsi pelajar Jabatan Hospitaliti. Oleh kerana matlamat kurikulum pendidikan kini adalah untuk melahirkan graduan yang dapat memenuhi keperluan pihak industri, maka kajian ini dijalankan untuk menilai hubungan di antara keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan pelajar Jabatan Hospitaliti di Politeknik. Terdapat 13 kemahiran generik yang diperolehi daripada Kementerian Pelajaran dan Latihan Ontario (1997) dijadikan sebagai skop kepada instrumen kajian. Responden kajian terdiri daripada tiga pihak utama iaitu industri perhotelan di Malaysia yang melibatkan 40 buah hotel yang diwakili oleh MAH Chapter dan jawatankuasa dalam Malaysian Associated of Hotel (MAH), pensyarah Unit Hotel dan Katering dan pelajar semester akhir Diploma Hotel dan Katering di Politeknik Johor Bahru, Johor dan Politeknik Merlimau, Melaka. Kajian rintis yang dijalankan menunjukkan nilai Alpha Cronbach pada 0.8781. Data yang diperolehi dianalisis secara deskriptif dan inferensi dengan menggunakan perisian Statistical Package for Social Science (SPSS) versi 11.5. Melalui dapatan kajian, satu senarai berkenaan kemahiran generik yang diperlukan oleh industri perhotelan telah dapat dihasilkan. Selain itu, senarai kemahiran generik menurut persepsi pensyarah dan juga persepsi pelajar turut dihasilkan. Hasil statistik dan graf garis yang diperolehi menunjukkan terdapat perbezaan di antara kemahiran generik yang diperlukan oleh industri perhotelan di Malaysia dengan kemahiran generik menurut persepsi pensyarah dan persepsi pelajar Politeknik. Dapatan analisis menggunakan korelasi Pearson mendapati bahawa tidak terdapat perhubungan yang signifikan di antara kemahiran generik yang diperlukan oleh industri perhotelan dengan persepsi pensyarah dan persepsi pelajar. Namun begitu, terdapat hubungan yang signifikan di antara persepsi pensyarah dengan persepsi pelajar berkenaan dengan amalan kemahiran generik di Politeknik

Optimal control design for robust fuzzy friction compensation in a robot joint

This paper presents a methodology for the compensation of nonlinear friction in a robot joint structure based on a fuzzy local modeling technique. To enhance the tracking performance of the robot joint, a dynamic model is derived from the local physical properties of friction. The model is the basis of a precompensator taking into account the dynamics of the overall corrected system by means of a minor loop. The proposed structure does not claim to faithfully reproduce complex phenomena driven by friction. However, the linearity of the local models simplifies the design and implementation of the observer, and its estimation capabilities are improved by the nonlinear integral gain. The controller can then be robustly synthesized using linear matrix inequalities to cancel the effects of inexact friction compensation. Experimental tests conducted on a robot joint with a high level of friction demonstrate the effectiveness of the proposed fuzzy observer-based control strategy for tracking system trajectories when operating in zero-velocity regions and during motion reversals