An optimal solution for magnetohydrodynamic nanofluid flow over a stretching surface with constant heat flux and zero nanoparticles flux

This article examines the hydromagnetic three-dimensional flow of viscous nanoliquid. A bidirectional linear stretching surface has been used to create the flow. Novel features regarding Brownian motion and thermophoresis have been studied by employing Buongiorno model to examine the slip velocity of nanoparticle. Viscous liquid is electrically conducting subject to uniform applied magnetic field. Problem formulation in boundary-layer region is performed for low magnetic Reynolds number. Simultaneous effects of constant heat flux and zero nanoparticles flux conditions are utilized at boundary. Appropriate transformations correspond to the strongly nonlinear ordinary differential expressions. The resulting nonlinear systems have been solved through the optimal homotopy analysis method. Graphs have been sketched in order to analyze that how the temperature and concentration profiles are affected by various physical parameters. Further the coefficients of skin-friction and heat transfer rate have been numerically computed and discussed. Our findings show that the temperature distribution has a direct relationship with the magnetic parameter. Moreover, the temperature distribution and thermal boundary-layer thickness are higher for hydromagnetic flow in comparison with the hydrodynamic flow.

     

Iterative Identification Algorithms for Bilinear-in-parameter Systems by Using the Over-parameterization Model and the Decomposition

This paper focuses on the identification problem for a class of bilinear-in-parameter systems with an additive noise modeled by an autoregressive moving average process. By using the over-parameterization model, the special form of the bilinear term can be obtained by the model equivalent transformation. Then, we use a decomposition of the model into two synthetic models in order to separate the effect of the two sets of parameters, i.e., the coefficients of the nonlinear basis functions from the parameters of the colored noise. Moreover, two decomposition based iterative algorithms are proposed to identify the unknown parameters. A numerical example is presented to confirm the effectiveness of the proposed methods.     

Least Squares based Iterative Parameter Estimation Algorithm for Stochastic Dynamical Systems with ARMA Noise Using the Model Equivalence

International Journal of Control, Automation and Systems - By means of the model equivalence theory, this paper proposes a model equivalence based least squares iterative algorithm for estimating...     

Maximum likelihood‐based gradient estimation for multivariable nonlinear systems using the multiinnovation identification theory

This article considers the identification problems of multivariable input nonlinear systems with unmeasured disturbances. For the identification difficulty caused by the crossproducts between the parameters of the linear block and the nonlinear block, the key term separation technique is adopted to separate the parameters of the nonlinear block from the parameters of the linear block. By combining the model decomposition technique and the hierarchical identification principle, a key term separation‐based maximum likelihood recursive extended stochastic gradient algorithm with reduced computational complexity is presented to estimate all the parameters directly. By introducing the multiinnovation identification theory, a key term separation‐based maximum likelihood multiinnovation extended stochastic gradient algorithm is proposed to improve the parameter estimation accuracy. The simulation results illustrate the effectiveness of the proposed methods.     

Distributed Observer‐based Stabilization of Nonlinear Multi‐agent Systems with Sampled‐data Control

In this paper, the distributed observer‐based stabilization problem of multi‐agent systems under a directed graph is investigated. Distributed observer‐based control protocol with sampled‐data information is proposed. The dynamics of each agent contain a nonlinear part, which is supposed to be general Lipschitz. In order to stabilize the states of the whole network, all the nodes utilize the relative output estimation error at sampling instants and only a small fraction of nodes use the absolute output estimation error additionally. By virtue of the input‐to‐state stability (ISS) property and the Lyapunov stability theory, an algorithm to design the control gain matrix, observer gain matrix, coupling strength as well as the allowable sampling period are derived. The conditions are in the form of LMIs and algebraic inequality, which are simple in form and easy to verify. Some further discussions about the solvability of obtained linear matrix inequalities (LMIs) are also given. Lastly, an example is simulated to further validate the obtained results.     

Coupled stochastic gradient identification algorithms for multivariate output-error systems using the auxiliary model

This paper considers the gradient based identification problem of a multivariate output-error system. By using the auxiliary model identification idea and the coupling identification concept, an auxiliary model based stochastic gradient (AM-SG) algorithm and a coupled AM-SG algorithm are presented. The results indicate that the parameter estimation errors converge to zero under the persistent excitation conditions. The simulation examples confirm the theoretical results.     

Global exponential stability of antiperiodic solutions for impulsive discrete‐time Markovian jumping stochastic BAM neural networks with additive time‐varying delays and leakage delay

This paper presents a global exponential stability of antiperiodic solution for a class of impulsive discrete‐time Markovian jumping stochastic bidirectional associative memory neural networks with additive time‐varying delays and leakage delay. By utilizing the Lyapunov‐Krasovskii functional and contraction mapping principle, several sufficient conditions and linear matrix inequalities are derived for verifying globally exponentially stable in the mean square. There is a new delay‐dependent criterion for checking the existence, uniqueness, and global stability for antiperiodic solution. Meantime, by using the numerically efficient MATLAB Toolbox, simulation examples are offered to show the effectiveness and usefulness of the obtained result.     

Hierarchical recursive signal modeling for multifrequency signals based on discrete measured data

This paper studies the problem of parameter estimation for the multifrequency sine signals, which have multiple characteristic parameters such as the amplitudes, phases, and frequencies. It is noted that the signal output is nonlinear with respect to the phase and frequency parameters while it is linear with respect to the amplitude parameters. This feature inspires us to separate all of the characteristic parameters into a linear parameter set and a nonlinear parameter set, where the linear set is composed of the amplitude parameters and the nonlinear set is composed of the phase parameters and the frequency parameters. After the parameter separation, two identification submodels are constructed for optimizing the linear parameter set and the nonlinear parameter set. Then the nonlinear identification model becomes a linear identification submodel and a nonlinear identification submodel. Therefore, the nonlinear optimization for minimizing the objective function is converted into the combination of the quadratic optimization and nonlinear optimization. Based on the separable identification submodels, a recursive least squares subalgorithm and a recursive gradient subalgorithm are proposed for identifying the linear parameters and nonlinear parameters, respectively. Moreover, an interactive estimation algorithm is designed to remove the related parameter sets between the subalgorithms and a hierarchical identification method is presented by combining the subalgorithms. For the purpose of tracking the time-varying, a forgetting factor is introduced to improve the convergence speed. The numerical examples are provided to qualify the performance of the proposed method based on some performance measures.     

Design Principles-Based Interactive Learning Tool for Solving Nonlinear Equations

Interactive learning tools can facilitate the learning process and increase student engagement, especially tools such as computer programs that are designed for human-computer interaction. Thus, this paper aims to help students learn five different methods for solving nonlinear equations using an interactive learning tool designed with common principles such as feedback, visibility, affordance, consistency, and constraints. It also compares these methods by the number of iterations and time required to display the result. This study helps students learn these methods using interactive learning tools instead of relying on traditional teaching methods. The tool is implemented using the MATLAB app and is evaluated through usability testing with two groups of users that are categorized by their level of experience with root-finding. Users with no knowledge in root-finding confirmed that they understood the root-finding concept when interacting with the designed tool. The positive results of the user evaluation showed that the tool can be recommended to other users.     

LMI-based approach to stability analysis for fractional-order neural networks with discrete and distributed delays

This paper is concerned with the asymptotic stability of the Riemann–Liouville fractional-order neural networks with discrete and distributed delays. By constructing a suitable Lyapunov functional, two sufficient conditions are derived to ensure that the addressed neural network is asymptotically stable. The presented stability criteria are described in terms of the linear matrix inequalities. The advantage of the proposed method is that one may avoid calculating the fractional-order derivative of the Lyapunov functional. Finally, a numerical example is given to show the validity and feasibility of the theoretical results.