This paper is concerned with the problem of adaptive control for a class of stochastic nonlinear systems with Markovian switching, where the upper bounds of nonlinearities of stochastic Markovian jump systems are assumed to be unknown. Firstly, an adaptation law is developed to estimate these unknown parameters. Then, a class of adaptive state feedback controller is proposed such that not only the estimated errors are bounded almost surely but also, the states of the resulting closed-loop system are asymptotically stable almost surely. Finally, a numerical example is given to show the validity of the results.Copyright © 2012 John Wiley & Sons, Ltd.

For the multisensor single-channel autoregressive moving average (ARMA) signal with colored measurement noise, when the partial model parameters and the noise variance are unknown, a self-tuning fusion Kalman filter weighted by scalar is presented based on the ARMA innovation model by the modern time series analysis method. With the application of the recursive instrumental variable algorithm and the Gevers–Wouters iterative algorithm with dead band, the information fusion estimators for the unknown model parameters and noise variance are obtained, and their consistence is proved by the existence and continuity theorem of implicit function. Then, substituting them into the optimal weighted fusion Kalman filter, one can obtain the corresponding self-tuning weighted fusion Kalman filter. Further, with the application of the dynamic variance error system analysis method, the convergence of the self-tuning Lyapunov equations for filtering error cross-covariances is proved. With the application of the dynamic error system analysis method, it is rigorously proved that the self-tuning weighted fusion Kalman filter converges to the optimal weighted fusion Kalman filter in a realization; that is, it has asymptotic optimality. A simulation example shows its effectiveness.Copyright © 2012 John Wiley & Sons, Ltd.

This paper is concerned with the state estimation for a kind of nonlinear multirate multisensor asynchronous sampling dynamic system. There are N sensors observing a single target independently at multiple sampling rates, and the dynamic system is formulated at the highest sampling rate. Observations are obtained asynchronously, and each sensor may lose data randomly at a certain probability. The fused state estimate is generated using multiscale system theory and the modified sigma point Kalman filter. It is shown that our main results improve and extend the existing sigma point Kalman filter for which the samples are obtained multirate nonuniformly. Measurements randomly missing with Bernoulli distribution could also be allowed in this paper. Finally, the feasibility and efficiency of the presented algorithm is illustrated by a numerical simulation example.Copyright © 2012 John Wiley & Sons, Ltd.

The input impedance of ultra-high frequency radio frequency identification tag varies with the received power on the chip. It will induce impedance mismatch between the receiver antenna and microchip, thus drastically affect the performance of communication. In this paper, a low cost and fully integrated automatic impedance matching system was presented to solve this problem. It consists of two control loops for independent control of the real and imaginary parts of impedance. The first control loop realizes resistance correction using a parallel LC tuning network, whereas the second control loop achieves reactance compensation using a series LC tuning network. In both loops, the mismatch information is detected for direct control of the variable elements, varactors, which are tuned in a sequential manner. For unambiguous control of the resistance correction, the sign of the intermediate reactance is used as a secondary control criterion to enforce operation into a stable region. The functionality of the proposed automatic matching system was verified for different input impedances of a specifically semi-ultra wideband ultra-high frequency radio frequency identification tag as the available input power varies. The results indicate that all matched impedances are clustered around the target impedance 50 + j0 Ω after acquisition of both loops. Copyright © 2012 John Wiley & Sons, Ltd.

The performance of conventional linear algorithms in active noise control applications deteriorates facing nonlinearities in the system mainly because of loudspeakers. On the other hand, fuzzy logic and neural networks are good candidates to overcome this drawback. In this paper, the acoustic attenuation of noise in a rectangular enclosure with a flexible panel and five rigid walls is presented both theoretically and experimentally using filtered gradient fuzzy neural network (FGFNN) error back propagation algorithm in which the secondary path effect is implemented in derivation of updating rules. Considering this effect in updating rules leads to faster convergence and stability of the active noise control system. On the other hand, the primary path in the investigated system comprises an identified nonlinear model of loudspeaker inside the aforementioned box, parameters of which vary with the input current. The loudspeaker is identified using series-parallel neural network model identification method. As a comparison, the performance of filtered-x least mean squares and FGFNN algorithms are compared. It is observed that FGFNN controller exhibits far better results in the presence of loudspeakers with nonlinear behavior in primary path.Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, a novel sliding mode control theory-based learning algorithm is proposed to train an interval type-2 fuzzy neural network using type-2 fuzzy triangular membership functions. The structure considered is a type-2 Takagi–Sugeno–Kang fuzzy logic system in which the antecedents are type-2 fuzzy sets, and consequents are crisp numbers (A2-C0). In the proposed learning algorithm, instead of trying to minimize an error function as is generally performed, the weights of the fuzzy neural network are tuned by the proposed algorithm in a way that the error is enforced to satisfy a stable equation. The parameter update rules to achieve this are derived, and the convergence of the parameters is proved by the use of Lyapunov stability method. To illustrate the applicability and the efficacy of the proposed method, we tested it on the velocity control of an electro hydraulic servo system in the presence of flow nonlinearities and internal friction. The motivation behind testing the proposed learning algorithm on this system is that it contains several nonlinearities that limit the ability of conventional controllers in achieving a satisfactory performance. The simulation studies indicate that the type-2 fuzzy neuro structure with the proposed learning algorithm results in a better performance than its type-1 fuzzy counterpart. Moreover, the proposed learning algorithm is easy to implement because of its simple structure, which makes it less complicated than the other learning algorithms seen in literature. Copyright © 2012 John Wiley & Sons, Ltd.

This paper studies an unsupervised approach for online adaptation of electroencephalogram (EEG) based brain–computer interface (BCI). The approach is based on the fuzzy C-means (FCM) algorithm. It can be used to improve the adaptability of BCIs to the change in brain states by online updating the linear discriminant analysis classifier. In order to evaluate the performance of the proposed approach, we applied it to a set of simulation data and compared with other unsupervised adaptation algorithms. The results show that the FCM-based algorithm can achieve a desirable capability in adapting to changes and discovering class information from unlabeled data. The algorithm has also been tested by the real EEG data recorded in experiments in our laboratory and the data from other sources (set IIb of the BCI Competition IV). The results of real data are consistent with that of simulation data. Copyright © 2011 John Wiley & Sons, Ltd.

This paper proposes a strategy to design a minimal and fault-tolerant sensor network for observability of complex systems. Complex systems are large-scale dynamic systems composed of interconnected subsystems. The objective is to determine the sensors to be used so that the system is observable while minimizing the number of sensors. The strategy is based on breaking down complex systems into interconnected subsystems. System breakdown helps in treating each subsystem separately and allows using reduced-order observers rather than a large-scale observer for the overall system. An academic example is given to illustrate the proposed strategy.Copyright © 2011 John Wiley & Sons, Ltd.

A recursive algorithm based on the use of Gauss–Seidel iterations is introduced to adjust the parameters of a self-tuning controller for minimum phase and a class of nonminimum phase discrete-time systems. The proposed algorithm is called the Recursive Gauss–Seidel (RGS) algorithm and is used to update the controller parameters directly. The use of the RGS algorithm with a generalized minimum variance control law is also given for nonminimum phase systems, and a forgetting factor is used to track the time-varying parameters. Furthermore, the overall stability of the closed-loop system is proven by using the Lyapunov stability theory. Using computer simulations, the performance of the RGS algorithm is examined and compared with the widely used recursive least squares algorithm.Copyright © 2011 John Wiley & Sons, Ltd.

In this work, we present an adaptive observer/controller for a multiple degree of freedom robotic plant without velocity measurement and without knowledge of plant parameter values. For this considered plant, we propose and present an adaptive observer/controller that estimates or observes the velocity and drives the position tracking error to zero. We prove that the combined tracking error and observer error converges to zero globally asymptotically and that all closed-loop signals remain bounded. A contribution of the present paper, as compared with previous work for this same plant, can be deemed to be the fact that, to the best of our knowledge, the present paper is the first proven globally asymptotic result for this plant for which the size of the control torque does not increase exponentially with respect to the size of the tracking error. The control torque is discontinuous, however, only at isolated time instants. No sliding modes are used. Copyright © 2011 John Wiley & Sons, Ltd.

This paper considers two consensus problems of multiple nonlinear systems. In the first consensus problem, distributed control laws for multiple nonlinear systems are proposed such that the state of each system converges to a constant agreement vector with the aid of communications between systems. In the second consensus problem, distributed robust/adaptive control laws for multiple nonlinear systems are proposed such that the state of each system converges to the state of a reference system whose state is available to a portion of multiple systems. Simulation results show the effectiveness of the proposed control laws. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, the controllability for a type of discrete-continuous systems with delays is investigated. The solution of such systems based on variation of parameters is derived. Several sufficient and necessary algebraic conditions for the controllability of the system as well as the relation among these conditions are established. It is also shown that the delayed input contributes to achieving the controllability of discrete-continuous systems. A numerical example is provided to illustrate the effectiveness of the proposed methods. Copyright © 2011 John Wiley & Sons, Ltd.

In the nondestructive testing of materials, ultrasonic imagery can detect and characterize defects that are present in a structure. Data are displayed in the form of images, and processing algorithms can be applied for automatic detection and characterization. However, when using diffracted waves, the amplitude is often too low, and the signals are difficult to distinguish from the noise. Other times, the volume of data requires significant computation time.

In this paper, we propose a method that can avoid image formation by replacing it with a sparse matrix and significantly reducing the amount of data to process; this allows for the enhancement and the automation of the detection of thin and flat defects such as cracks. The elements of the sparse matrix form a curve, which is sufficient to characterize defects in many cases. These elements are selected from diffracted signals using the split-spectrum processing method. In this way, the signal-to-noise ratio is improved, and the position of the echo signal is accurately determined.

When a crack is present in a material, the points of the sparse matrix form a parabola and classical tools of pattern recognition such as the Hough transform can detect it, thus providing significant help in decision-making processes. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, the problem of state estimation in an asynchronous distributed multi-sensor estimation (ADE) system is considered. In such an ADE system, the state of a plant of interest is estimated by a group of local estimators. Each local estimator based, for example, on a Kalman filter, performs fusion of data from its local sensor and other (remote) processors to compute possibly best state estimates. In performing data fusion, however, two important issues need to be addressed, namely, the problem of asynchronism of local processors and the one of unknown correlation between asynchronous data in local processors. Consequently, there are two main contributions proposed in this paper. The first is a method to deal with asynchronous discrete-time data based on a continuous-time stochastic plant model. The second contribution is an asynchronous distributed data-fusion algorithm. Simulated experiments illustrate the effectiveness of the proposed ADE approach. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, for a class of multivariable systems with strong couplings, a robust self-tuning PI decoupling controller is developed by combining a self-tuning PI controller with a feedforward decoupling compensator and a filter. To determine the gains and other parameters of the PI decoupling controller, we first introduced a reduced order model. The parameters of the reduced order model are identified by using a normalized projection algorithm with dead zone. The gains of the PI controller together with other parameters are tuned online according to the certainty equivalent principle. By resorting to time-varying operation, we presented the bounded-input bounded-output stability conditions and convergence conditions of the closed-loop system. Simulation results on a synthetic system and a twin-tank level system show the effectiveness of the proposed method. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we proposed a method for improving the recognition performance of 145 prominent consonant–vowel (CV) units in Indian languages for low bit-rate coded speech. Proposed CV recognition method is carried out in two levels to reduce the similarity among a large number of CV classes. In the first level, vowel category of CV unit will be recognized, and in the second level, consonant category will be recognized. At each level of the proposed method, complementary evidences from support vector machine and hidden Markov models are combined to enhance the recognition performance. Effectiveness of the proposed two-level CV recognition method is demonstrated by performing the recognition of isolated CV units and CV units collected from the Telugu broadcast news database. In this work, vowel onset point (VOP) is used as an anchor point for extracting accurate features from the CV unit. Therefore, a method is proposed for accurate detection of VOP in clean and coded speech. The proposed VOP detection method is based on the spectral energy in 500–2500 Hz frequency band of the speech segments present in the glottal closure region. Speech coders considered in this work are GSM full rate (ETSI 06.10), CELP (FS-1016), and MELP (TI 2.4 kbps). Significant improvement in CV recognition performance is achieved using the proposed two-level method compared with the existing methods under both clean and coded conditions. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, several concepts of switching frequency are introduced to analyze the properties and performance of switched systems in infinite as well as finite-time intervals. The observation is very motivating that different system properties and performances depend on different switching frequencies. Sufficient conditions ensuring asymptotic stability, ℓ₂ gain performance, and state boundness are derived on the basis of the notions of switching frequency, respectively. Then, on the basis of the analysis results, the control synthesis problems are addressed. LMI-based design algorithms are proposed to meet different control synthesis requirements. Numerical design examples are provided to demonstrate our results. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, a new approach for cancelation of a multiharmonic disturbance is proposed. Compared with a number of known results in this paper, the disturbance cancelation problem is solved when the output variable is measured only, a relative degree of the plant is arbitrary and the control channel has delay. The numerical example is presented to illustrate the theoretical result. The reaction wheel pendulum on a movable platform is considered as the plant to demonstrate that the proposed approach is realizable and can be plugged in practice. The second goal is the development of mechatronic applications for use in education. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, the risk-sensitive filtering method that relaxes the dependence on model accuracy is extended to nonlinear Markov jump systems (MJSs). In the method, the so-called reference probability technique together with particle approximation is utilized to derive the risk-sensitive filter in nonlinear non-Gaussian framework. The novelty of the proposed approach is that a ‘risky’ interacting resampling step is performed to both moderate the modeling uncertainties and to solve the problem of particle explosion. A designer-chosen parameter named risk-sensitive parameter allows us to make a trade-off between the filtering accuracy for the nominal model and the robustness to uncertainties. With a meaningful example, it shows that the developed method can outperform the widely used method-particle filter and interacting multiple model-particle filter in nonlinear MJSs with uncertainties. Copyright © 2011 John Wiley & Sons, Ltd.

This paper is concerned with the problems of stability analysis, H_∞ performance analysis, and robust H_∞ filter design for uncertain Markovian jump linear systems with time-varying delays. The purpose is to improve the existing results on these problems. Firstly, a new delay-dependent stability criterion is obtained on the basis of a novel mode-dependent Lyapunov functional. Secondly, a new delay-dependent bounded real lemma (BRL) is derived. It is shown that the presented stability criterion and the BRL are less conservative than the existing ones in the literature. Thirdly, with the new BRL, delay-dependent conditions for the solvability of the addressed H_∞ filtering problem are given. All the results obtained in this paper are expressed by means of strict linear matrix inequalities. Three numerical examples are provided to demonstrate the utility of the proposed methods.Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we provide a solution to the problem by considering input time delays in dynamic interactions. Each local controller, designed simply on the basis of the model of each subsystem by using filtered transformation and standard backstepping technique, only employs local information to generate control signals. The robustness of decentralized adaptive controllers is established. It is shown that the designed decentralized adaptive backstepping controllers can globally stabilize the overall interconnected system asymptotically. The L₂ and L_∞ norms of the system outputs are also established as functions of design parameters. This implies that the transient system performance can be adjusted by choosing suitable design parameters. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, a robust exponential l₂ −l_∞ filtering problem is addressed for discrete-time switched systems with polytopic uncertainties. The purpose of robust exponential l₂ −l_∞ filtering is to design a filter such that the resulting filtering error system is robustly exponentially stable with a decay rate and a prescribed exponential l₂ −l_∞ performance index. The robust exponential l₂ −l_∞ filtering problem is solved via an average dwell time approach. Sufficient conditions in terms of strict LMI are derived for checking the robust exponential stability of a filter. An explicit expression for the desired robust exponential filter is also given. Finally, a numerical example is provided to demonstrate the potential and effectiveness of the proposed method.Copyright © 2011 John Wiley & Sons, Ltd.

Based on hybrid process modeling, off-line optimization and neural control scheme (NCS), the combined system for off-line optimization and adaptive adjustment of cutting parameters is built. This is an adaptive control system controlling the cutting force by digital adaptation of cutting parameters. In this way, it compensates all disturbances during the cutting process, prevents excessive tool wear, and maintains a high chip removal rate. It is the combination of these methods that yields accurate force control. The basic control principle is based on the NCS consisting of two neural identifiers of the process dynamics and feedback controller. An overall procedure of hybrid modeling of cutting process, used for working out the computer numerical control (CNC) milling simulator has been prepared. CNC simulator is used to evaluate the controller design before conducting experimental tests. Numerous simulations and experiments have been conducted to confirm the efficiency of this control architecture. The experimental results show that not only does the end-milling system with the design controller have high robustness and global stability, but also the machining efficiency of the end milling system with the proposed controller is 27% higher than for traditional CNC milling system. Copyright © 2011 John Wiley & Sons, Ltd.

A general p-shift linear optimal finite impulse response (FIR) estimator is proposed for filtering (p  =  0), p-lag smoothing (p  <  0), and p-step prediction (p  >  0) of discrete time-varying state-space models. An optimal solution is found in the batch form with the mean square initial state function self-determined by solving the discrete algebraic Riccati equation. An unbiased batch solution is shown to be independent on noise and initial conditions. The mean square errors in both the optimal and unbiased estimates have been determined along with the noise power gain and estimate error bound. The following important inferences have been made on the basis of numerical simulation. Unlike the time-invariant Kalman filter, the relevant optimal FIR one is very less sensitive to noise, especially when N  ≫ 1. Both time varying, the optimal FIR and Kalman estimates trace along almost the same trajectories with similar errors and sensitivities to noise. Overall, the optimal FIR estimator demonstrates better robustness than the Kalman one against faults in the noise description. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we study the effect of noisy channels on the transient performance of diffusion adaptive network with least-mean squares (LMS) learning rule. We first drive the update equation of diffusion LMS which incorporates the effects of noisy channels. Then, using the framework of fundamental weighted energy conservation relation, we derive closed-form expressions for learning curves in terms of mean-square deviation and excess mean-square error. We also find the mean and mean-square stability bounds of step-size for diffusion LMS with noisy channels. We show that although noisy channels affect the performance of the diffusion LMS network, the stability bounds of the step-size are the same form as in the ideal channels case. The derived closed-form expressions are shown to provide a good match with values found by simulation. Copyright © 2011 John Wiley & Sons, Ltd.

This paper presents the application of an adaptive predictive control system, entitled ‘Steam Temperature Optimizer’ (STO), in the combined cycle of the Empresa Nacional de Electricidad SA at Barranco de Tirajana in the Canary Islands. This combined cycle comprises two gas turbines of 70 MW each, two recovery boilers, and one steam turbine also of 70 MW. The STO was applied to the control of the temperature of the high-pressure recovery boilers and integrated in parallel with the plant's distributed control system by means of object linking and embedding for process control communication. The STO control strategies, designed to deal with the specific attemperation control problems and plant instrumentation limitations, are presented and the experimental results analyzed in comparison with those obtained by the existing PID-based system. The STO improved significantly the control precision and the stability of the steam temperature and confirmed adaptive predictive control as a reliable self-tuning methodology for this kind of process. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, robust fault detection based on adaptive threshold generation of a non-linear system described by means of a linear parameter-varying (LPV) model is addressed. Adaptive threshold is generated using an interval LPV observer that generates a band of predicted outputs taking into account the parameter uncertainties bounded using intervals. An algorithm that propagates the uncertainty based on zonotopes is proposed. The design procedure of this interval LPV observer is implemented via pole placement using linear matrix inequalities. Finally, the minimum detectable fault is characterized using fault sensitivity analysis and residual uncertainty bounds. Two examples, one based on a quadruple-tank system and another based on a two-degree of freedom helicopter, are used to assess the validity of the proposed fault detection approach. Copyright © 2011 John Wiley & Sons, Ltd.

A fault detection and isolation (FDI) filter design method is proposed for linear parameter-varying (LPV) systems, which are subject to abrupt changes in their structure. Such a phenomenon is modeled by a finite state Markov chain whose outcome is supposed to be directly available along with its rates transition matrix. The FDI filter is designed as a bank of ℋ₋/ℋ_∞ Luenberger observers, derived by optimizing frequency conditions that ensure guaranteed levels of disturbance rejection, fault sensitivity and are capable to discriminate anomalous events belonging to different fault classes. It is proved that, by resorting to stochastic stability concepts, the design method can be recast as a linear matrix inequality programming program in the observer bank gains. The resulting residual generator is a jump parameter-dependent observer jointly exploiting the available measures on the deterministic plant parameter and on the instantaneous Markov chain realization. An FDI threshold logic is also proposed in order to reduce the generation of false alarms. The effectiveness of the design technique is illustrated via a numerical example. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, a model-based fault estimation method for a particular class of discrete-time descriptor linear parameter-varying systems is developed. The main contribution of this work consists in the design of an observer that performs simultaneously both, the states estimation and the fault magnitude vectors, considered as unknown inputs. The conditions for the existence of such observer are given. Such conditions guarantee the observer stability and they are proved through a Lyapunov analysis combined with a linear matrix inequalities formulation. The fault estimation scheme is evaluated through numerical simulations. Copyright © 2011 John Wiley & Sons, Ltd.

This paper investigates the design of robust‡ fault detection and isolation filters for linear parameter-varying systems modeled in a linear fractional representation fashion. The goal is to obtain structured fault detection filters with enhanced fault transmission H₋ gain and large H_∞ nuisance attenuation. It is shown by means of the scaling matrices technique and the projection lemma that the synthesis of the residual structuring and the filter state-space matrices can be performed simultaneously using linear matrix inequality optimization techniques. Computational aspects are discussed and it is shown that the proposed solution is structurally well defined. Closed-loop time simulations demonstrate the efficiency of the proposed method. Copyright © 2011 John Wiley & Sons, Ltd.

The main contribution of this paper is the design of a polytopic unknown inputs proportional integral observer (UIPIO) for linear parameter-varying (LPV) descriptor systems. This observer is used for actuator fault detection and isolation. The proposed method is based on the representation of the LPV descriptor systems in a polytopic form. Its parameters evolve in an hypercube domain. The designed polytopic UIPIO is also able to estimate both the states and the unknown inputs of the LPV descriptor system. Stability conditions of such observer are expressed in terms of linear matrix inequalities. An example illustrates the performances of such polytopic UIPIO. Copyright © 2011 John Wiley & Sons, Ltd.

This paper concerns the Bayesian tracking of slowly varying parameters of a linear stochastic regression model. The modelled and predicted system output is assumed to possess time-varying mean value, whereas its dynamics are relatively stable. The proposed estimation method models the system output mean value by time-varying offset. It formulates three extreme hypotheses on model parameters' variability: (i) no parameter varies; (ii) all parameters vary; and (iii) the offset varies. The Bayesian paradigm then provides a mixture as posterior distribution, which is appropriately projected to a feasible class. Exponential forgetting at ‘second’ hypotheses level allows tracking of slow variations of respective hypotheses.

The developed technique is an example of a general procedure called partial forgetting. Focus on a simple example allows to demonstrate essence of the approach. Moreover, it is important per se as it corresponds with a varying load of otherwise (almost) time-invariant dynamic system. Copyright © 2011 John Wiley & Sons, Ltd.

This paper considers the problem of decentralized adaptive robust stabilization of a class of interconnected time-delay systems with arbitrarily bounded matched but limitedly bounded unmatched uncertainties. A new class of decentralized adaptive controllers based on Lyapunov–Krasovskii functional is proposed that guarantees bounded stability of the system and ensures nonfragileness of the controller to perturbations in its nonadaptive gain factor. The existence of such controllers is formulated in the LMI framework besides being presented using the Algebraic Riccati Equations. A numerical example is considered to illustrate the applicability and effectiveness of the proposed controller. Copyright © 2011 John Wiley & Sons, Ltd.

Discrete-time Volterra models are widely used in various application areas. Their usefulness is mainly because of their ability to approximate to an arbitrary precision any fading memory nonlinear system and to their property of linearity with respect to parameters, the kernels coefficients. The main drawback of these models is their parametric complexity implying the need to estimate a huge number of parameters. Considering Volterra kernels of order higher than two as symmetric tensors, we use a parallel factor (PARAFAC) decomposition of the kernels to derive Volterra-PARAFAC models that induce a substantial parametric complexity reduction. We show that these models are equivalent to a set of Wiener models in parallel. We also show that Volterra kernel expansions onto orthonormal basis functions (OBF) can be viewed as Tucker models that we shall call Volterra-OBF-Tucker models. Finally, we propose three adaptive algorithms for identifying Volterra-PARAFAC models when input–output signals are complex-valued: the extended complex Kalman filter, the complex least mean square (CLMS) algorithm and the normalized CLMS algorithm. Some simulation results illustrate the effectiveness of the proposed identification methods. Copyright © 2011 John Wiley & Sons, Ltd.

The lattice of component subsets is a very useful structure for addressing fault tolerance and architecture design problems for systems described as a set of components. This paper presents a number of concepts and techniques that are associated with this lattice to evaluate the degree of fault tolerance of a given property and to classify components with respect to their usefulness for this property. Being very general, the approach needs no assumption on the system, nor on the properties to be satisfied, and allows both deterministic and probabilistic measures to be used. A sensor selection example illustrates the practical use of the proposed tools. Copyright © 2011 John Wiley & Sons, Ltd.

Minimum output energy (MOE) algorithm is a widely used adaptive algorithm for blind adaptation of infinite impulse response (IIR) filters. In this paper, we show that the MOE algorithm is not suitable for blind adaptation of the complex-valued IIR equalizer for digital vestigial sideband signals, whereas the constant modulus algorithm successfully achieves blind adaptation of the IIR equalizers when MOE fails. Because of the difficulty in analyzing IIR equalizers, the analysis is limited to a simple two-tap channel case. For more general multitap channel cases, the performance of a complex constant modulus algorithm IIR is evaluated through simulation. Copyright © 2011 John Wiley & Sons, Ltd.

This paper deals with numerical performance of linear minimum mean square error estimator for discrete-time Markovian jump linear systems. We propose a fast array algorithm to compute this estimator in virtue of the reduction of the computational effort that it provides, if compared with the standard array algorithms. It is particularly useful for this case because the dimension of the linear minimum mean square error estimator increases proportionally to the number of Markovian states. Numerical examples are shown to illustrate the effectiveness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd.