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Summary: In this paper, the problem of simultaneous identification and predictive control of nonlinear dynamical systems using self‐recurrent wavelet neural network (SRWNN) is addressed. The structure of the SRWNN is a modification of the wavelet neural network (WNN). Unlike WNN, the neurons present in the hidden layer of SRWNN contain the weighted self‐feedback loops. Dynamic back‐propagation algorithm is employed to derive the necessary parameter update equations. To further improve the convergence speed of the parameters, a time‐varying (adaptive) learning rate is used. Four simulation examples are considered for testing the effectiveness of the proposed method. Furthermore, some disturbance rejection tests are also performed on the proposed method. The results obtained through the simulation study confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This research proposes a pattern/shape-similarity-based clustering approach for time series prediction. This article uses single hidden Markov model (HMM) for clustering and combines it with soft computing techniques (fuzzy inference system/artificial neural network) for the prediction of time series. Instead of using distance function as an index of similarity, here shape/pattern of the sequence is used as the similarity index for clustering, which overcomes few of the shortcomings associated with distance-based clustering approaches. Underlying hidden properties of time series are captured with the help of HMM. The prediction method used here exploits the pattern identification prowess of the HMM for cluster selection and the generalization and nonlinear modeling capabilities of soft computing methods to predict the output of the system. To see the validity of the proposed method in the real-life scenario, it is tested on four different time series. The first is a benchmark Mackey-Glass time series, which is tested for delay parameters τ = 17 and τ = 30. The remaining time series are monthly sunspot data time series, Laser data time series and the last is Lorenz attractor time series. Simulation results show that the proposed method provide a better prediction performance in comparison with the existing methods. [ABSTRACT FROM AUTHOR]

An inherent characteristic of the many imaging modalities such as fluorescence microscopy and other microscopic modalities is the presence of intrinsic Poisson noise that may lead to degradation of the captured image during its formation. A nonlinear complex diffusion-based filter adapted to Poisson noise is proposed in this paper to restore and enhance the degraded microscopic images captured by imaging devices having photon limited light detectors. The proposed filter is based on a maximum a posterior approach to the image reconstruction problem. The formulation of the filtering problem as maximisation of a posterior is useful because it allows one to incorporate the Poisson likelihood term as a data attachment which can be added to an image prior model. Here, the Gibb's image prior model-based on energy functional defined in terms of gradient norm of the image is used. The performance of the proposed scheme has been compared with other standard techniques available in literature such as Wiener filter, regularised filter, Lucy-Richardson filter and another proposed nonlinear anisotropic diffusion-based filter in terms of mean square error, peak signal-to-noise ratio, correlation parameter and mean structure similarity index map. The results shows that the proposed complex diffusion-based filter adapted to Poisson noise performs better in comparison to other filters and is better choice for reduction of intrinsic Poisson noise from the digital microscopic images and it is also well capable of preserving edges and radiometric information such as luminance and contrast of the restored image. [ABSTRACT FROM AUTHOR]

In this letter, robust stability analysis, of the closed- loop pulsewidth modulated push-pull dc-dc converter with a state feedback control is presented using simple frequency-domain conditions based on the Hermite-Biehler theorem. The proposed technique for checking robust stability is efficient, since there is no need to calculate all the uncertain values of the system and hence no need to formulate and test all four Kharitonov polynomials. [ABSTRACT FROM AUTHOR]

The analytical and numerical solutions of the non-linear exact Kushner filter are not possible, since the mean and variance evolutions are infinite dimensional and require the knowledge of the higher-order moment evolutions. The approximate filters seem to preserve some of the qualitative characteristics of the exact filter. In this paper, evolutions of conditional mean and conditional covariance of the third-order approximate filter for estimating the states of a non-linear dynamical system, especially accounting state-dependent and state-independent noise perturbations, are derived. In this analysis, we make a comparison of this filter with second-order Gaussian filter discussed in standard textbooks on non-linear filtering. This paper discusses Duffing filter, by taking up two different non-linear observation equations to demonstrate the effectiveness of the higher-order filters, i.e. third order, and second-order filters. Most notably, this paper is about examining the ability of the higher-order filters for estimating the states of the stochastically perturbed non-linear dynamical systems. In addition, the analytical findings are supported with numerical work generated by a simple, but effective, finite difference scheme. [ABSTRACT FROM AUTHOR]

This article deals with performance analysis and implementation of a four-quadrant chopper fed dc motor drive system. Control structure of the system is effected through a digital processor. The closed-loop control scheme of the drive utilizes the sliding mode controller (SMC) in its outer speed loop, while the inner loop is equipped with the Pulse Width Modulated (PWM) hysteresis current controller. An algorithmis evolved to eliminate the mechanical speed sensor. To simulate performance of the drive system, a mathematical model is developed. An experimental verification is carried out for the simulated performance. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid of Neural Network (NN) and Fast Traversal Filter (FTF) based controller in each area is used to determine the optimal parameters of Load Frequency Control (LFC) of a realistic two area power system. The two area power system is modeled considering the various non -linearities like governor dead band, generation rate constraint (GRC) and boiler dynamics. Input to the controller i.e. the error signal is divided into two parts- linear and non- linear. The linear part of the input signal is minimized by the FTF algorithm, whereas the non- linear part is minimized by the NN algorithm. The output of the controller is the sum of the outputs of NN and FTF networks. The proposed hybrid controller requires less number of samples for training of weights, thus making the system fast. This is highly desirable in power quality problems. The various components of power system are reduced to transfer functions and the system performance is analyzed for 1% step load perturbation in area1 with different controllersproportional and integral (PI), neural network (NN) and NN+FTF based controllers. The simulations demonstrate the fast and smooth performance of the power system with the proposed controller. Simulated results evince the superiority of the proposed hybrid controller. [ABSTRACT FROM AUTHOR]