Filter Design Problem Research Articles

Adaptive Neural Network-Based Filter Design for Nonlinear Systems With Multiple Constraints.

Filter design for nonlinear systems, especially time delayed nonlinear systems, has always been an important and challenging problem. This brief investigates the filter design problem of nonlinear systems with multiple constraints: time delay, actuator, and sensor faults, and a new adaptive neural network-based filter design method is proposed. Comparing with the existing works where there is a shortcoming that the designed filters contain unknown time delay(s), the design method proposed in this brief overcomes the shortcoming and only the estimation of the unknown time delay exists in the filter. Furthermore, not only the system states can be estimated, but also the unknown time delay with actuator and sensor faults can be estimated in this brief. Finally, simulation results are given to show the effectiveness of the proposed new design method.

Robust finite‐frequency filter design for linear uncertain systems using polynomially parameter‐dependent approach

This study investigates the problem of robust filter design for continuous-time linear uncertain systems affected by external noises in finite-frequency (FF) ranges. The objective is to design an admissible filter guaranteeing asymptotic stability of the filtering error system with a prescribed FF noise attenuation level. To this end, homogenous polynomially parameter-dependent Lyapunov functions are used to assess the stability of the filtering error system, and robust FF bounded realness lemma is used to describe the noise attenuation specifications. Polynomially parameter-dependent matrices and some scalars are introduced to facilitate filter design and reduce conservatism. Moreover, a sequential convex approximation method is used to replace the non-convex constrained set with a convex feasible one. On this basis, an iterative algorithm is developed to calculate the filter parameters. It is demonstrated that the proposed robust FF filter can achieve a better noise attenuation performance in the FF range than the existing ones in the literature. An example about F-18 aircraft model is given to illustrate the effectiveness of the proposed method.

A symbiotic organisms search algorithm-based design optimization of constrained multi-objective engineering design problems

PurposeIn line with computational technological advances, obtaining optimal solutions for engineering problems has become attractive research topics in various disciplines and real engineering systems having multiple objectives. Therefore, it is aimed to ensure that the multiple objectives are simultaneously optimized by considering them among the trade-offs. Furthermore, the practical means of solving those problems are principally concentrated on handling various complicated constraints. The purpose of this paper is to suggest an algorithm based on symbiotic organisms search (SOS), which mimics the symbiotic reciprocal influence scheme adopted by organisms to live on and breed within the ecosystem, for constrained multi-objective engineering design problems.Design/methodology/approachThough the general performance of SOS algorithm was previously well demonstrated for ordinary single objective optimization problems, its efficacy on multi-objective real engineering problems will be decisive about the performance. The SOS algorithm is, hence, implemented to obtain the optimal solutions of challengingly constrained multi-objective engineering design problems using the Pareto optimality concept.FindingsFour well-known mixed constrained multi-objective engineering design problems and a real-world complex constrained multilayer dielectric filter design problem are tackled to demonstrate the precision and stability of the multi-objective SOS (MOSOS) algorithm. Also, the comparison of the obtained results with some other well-known metaheuristics illustrates the validity and robustness of the proposed algorithm.Originality/valueThe algorithmic performance of the MOSOS on the challengingly constrained multi-objective multidisciplinary engineering design problems with constraint-handling approach is successfully demonstrated with respect to the obtained outperforming final optimal designs.

Controller design for active noise control of compressor by using the time window POCS technique

To design an active noise controller, the transfer function between the reference signal and the error signal should be measured and defined. When the noise source operates at specific frequencies, the transfer function can only be measured at the operating frequency and its harmonics. This transfer function has reliable values only at the operating frequency and its harmonic frequencies. The frequency domain controller is implemented as an FIR filter, and the coefficients are calculated using the inverse Fourier transform. At this time, the second half part of the coefficient values of the FIR filter is high due to the insufficient frequency resolution and the unreliable transfer function. As a result, the magnitude and phase response of the filter do not follow the design values. To overcome this problem in filter design, the time window projection onto convex sets (POCS) technique is proposed. POCS technique is utilized repeatedly to make the response of the filter to converge by setting the second half part of the filter to 0, and to reassign the response in the control frequency band. The response of the converged filter was verified by comparing it with that of a time-shifting filter and Wiener filter. As a result, the FIR filter designed using POCS showed high performance, regardless of the magnitude of the disturbance and the reference signal.

Metaheuristic Spatial Transformation (MST) for accurate detection of Attention Deficit Hyperactivity Disorder (ADHD) using rs-fMRI.

Accurate detection of neuro-psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD) using resting state functional Magnetic Resonance Imaging (rs-fMRI) is challenging due to high dimensionality of input features, low inter-class separability, small sample size and high intra-class variability. For automatic diagnosis of ADHD and autism, spatial transformation methods have gained significance and have achieved improved classification performance. However, they are not reliable due to lack of generalization in dataset like ADHD with high variance and small sample size. Therefore, in this paper, we present a Metaheuristic Spatial Transformation (MST) approach to convert the spatial filter design problem into a constraint optimization problem, and obtain the solution using a hybrid genetic algorithm. Highly separable features obtained from the MST along with meta-cognitive radial basis function based classifier are utilized to accurately classify ADHD. The performance was evaluated using the ADHD200 consortium dataset using a ten fold cross validation. The results indicate that the MST based classifier produces state of the art classification accuracy of 72.10% (1.71% improvement over previous transformation based methods). Moreover, using MST based classifier the training and testing specificity increased significantly over previous methods in literature. These results clearly indicate that MST enables the determination of the highly discriminant transformation in dataset with high variability, small sample size and large number of features. Further, the performance on the ADHD200 dataset shows that MST based classifier can be reliably used for the accurate diagnosis of ADHD using rs-fMRI.Clinical relevance- Metaheuristic Spatial Transformation (MST) enables reliable and accurate detection of neuropsychological disorders like ADHD from rs-fMRI data characterized by high variability, small sample size and large number of features.

Design of optimal low-pass filter by a new Levy swallow swarm algorithm

The swallow swarm optimization (SS) is a challenging method of optimization, which has a quicker convergence speed, not getting caught in the local extreme points. However, the SS suffers from a few shortcomings—(1) the movement speed of particles is not controlled suitably during the search due to the requirement of an inertia weight and (2) the less flexibility of variables does not permit to maintain a balance between the local and the global searches. To solve these problems, a new Levy swallow swarm optimization (SSLY) algorithm with the exploitation capability is proposed. This article also provides an optimal design methodology for the low-pass filter using the suggested SSLY technique. A new objective function is introduced to achieve the maximally flat frequency response, which is another important contribution to the field. The firefly algorithm (FA), the sine cosine algorithm (SCA) and the standard global optimizers—real coded genetic algorithm (GA), conventional particle swarm optimization (PSO), cuckoo search (CS) and SS, are considered for a comparison. The proposed SSLY outperforms the FA, SCA, GA, PSO, CS and SS algorithms. Results authenticate suitability of the proposed algorithm for solving the filter design problems in the FIR domain.

Quasi‐time‐dependent asynchronous filtering for discrete time switched systems via the event triggering mechanism

SummaryThis article is concerned with the quasi‐time‐dependent asynchronous filter design problem for a class of discrete‐time switched systems via the event‐triggering mechanism. Applying the quasi‐time‐dependent Lyapunov functions and the mode‐dependent average dwell time technique, an asynchronous filter is designed with a weighted performance index; the filter parameter matrices are quasi‐time‐dependent in each event‐triggering‐dependent sampling interval; both cases (Case 1: no more than one switching, Case 2: multiple switchings) are taken into account in this sampling interval, by which the assumption, that the maximal asynchronous period is not larger than the minimal dwell time, is relaxed in this article. Simulation examples are given to show the less conservatism and effectiveness of the proposed results.

Cascaded Form Sparse FIR Filter Design

In this paper, a cascaded form sparse finite impulse response (FIR) filter design algorithm is proposed. By decomposing a filter into two subfilters, the sparse filter design problem is reformulated as a quadratic constrained l 0 -norm minimization problem, which is further transformed to a convex problem through the introduction of a binary masking vector. An iterative forth-and-back search with sequence direction and error direction control is proposed to optimize the binary masking vector by minimizing the distance between the minimax approximation error and the specified upper bound. Experimental results show that the proposed design algorithm can improve the sparsity with comparable or even lower effective filter order.

A high-performance guidance filter scheme with exact dynamic modeling of a pitch-yaw gimballed seeker mechanism

This paper is concerned with the problem of guidance filter design for a homing air defense missile using measurements of an on-board pitch-yaw gimballed seeker mechanism. This problem possess a highly nonlinear dynamic which is influenced by various error sources such as seeker’s stabilization loop torque disturbances originated from pursuer’s accelerations and angular velocities, significant channel couplings, time-delayed and noisy measurements of optical sensor besides the unknown target maneuvers. To handle the inherent nonlinearity of problem a wide variety of assumptions are made in literature to derive simplified engagement kinematics. In present work, performance degradations which might occur due to these simplifications are highlighted with the aid of deriving a general dynamic formulation for a pitch-yaw gimbal assembly. Then, a novel filter dynamic is suggested which requires no simplifying assumption in contrast with all existing solutions. Taking the advantages of proposed filter design, some beneficial points on generating accurate proportional navigation guidance commands are also presented. Comparative simulation analyses demonstrate the effectiveness of novel exact filtering scheme in comparison with the prominent existing solutions respecting the Cramer-Rao bound of estimation problem. The results of study, are generally applicable for performance enhancement in track loop control design of high accurate pointing systems.

A sigmoid attractiveness based improved firefly algorithm and its applications in IIR filter design

Recently, a novel population-based optimisation algorithm, namely firefly algorithm (FA), which mimics the flashing and attraction behaviour of fireflies, has shown promising performance in solving global optimisation problems. However, the preliminary studies have shown that FA often gets stuck in local optima. In this paper, we investigate the reasons why the FA suffers from getting stuck in local optima; and then propose an improved firefly algorithm (IFA). These improvements are twofold: first, a sigmoid-based attractiveness is employed to reformulate its definition and strengthen its local refinement ability; second, a dynamic step parameter tuning strategy is designed to adjust the random search intensity and narrow the search space iteratively to strengthen its global search ability. The empirical results indicate IFA can well balance between the global exploration and the local exploitation, and provides the best solutions, at least the competitive results, for most of 12 global optimisation problems over other FA variants. Besides, by employing IFA to solve well-known infinite impulse response filter design problems, we evaluate the effectiveness and efficiency of IFA. The experimental results and comparisons show that IFA performs better than, at least as competent again, other meta-heuristics in terms of the solution accuracy, solution robustness, and convergence rate.

An Exact Characterization of the L 1/L₋ Index of Positive Systems and Its Application to Fault Detection Filter Design

In this brief, the problem of the $L_{1}/L_{-}$ fault detection for positive systems is revisited. In the existing literature, the $L_{1}$ -gain and $L_{-}$ index for positive systems are often characterized separately, and thus their linear programming descriptions involve different Lyapunov vectors. This casts the fault detection filter design as a bilinear optimization problem. To circumvent this obstacle, we first show that, for an externally positive system, the $L_{1}$ -gain and $L_{-}$ index are determined, respectively, by the largest and smallest column sums of the static gain matrices. Based on this fact, an exact characterization is given for the $L_{1}/L_{-}$ index for positive systems in terms of a linear program with equality constraints. The new characterization only involves one single Lyapunov vector, and thus renders the fault detection filter design problem convex. In addition, we find that the maximum fault sensitivity (characterized by the $L_{-}$ index from the fault to the residual) that can be achieved by the filter design approach is proportional to the required upper bound on the $L_{1}$ -gain from the disturbance to the residual. Finally, an illustrative example of a positive electric circuit is presented to show the effectiveness of the theoretical results.

A new design approach for nearly linear phase stable IIR filter using fractional derivative

In this paper, a new design method for digital infinite impulse response &#x0028 IIR &#x0029 filters with nearly linear-phase response is presented using fractional derivative constraints &#x0028 FDCs &#x0029 . In the proposed method, design problem of an IIR filter is constructed as the minimization of phase error between the desired and designed phase response of an allpass filter &#x0028 APF &#x0029 such that the designed lowpass filter &#x0028 LPF &#x0029 or highpass filter &#x0028 HPF &#x0029 yields less passband &#x0028 ep &#x0029 , and stopband errors &#x0028 es &#x0029 with optimal stopband attenuation &#x0028 As &#x0029 . In order to have accurate passband &#x0028 pb &#x0029 response, FDCs are imposed on appropriate reference frequency, where the optimality of these FDCs are ensured by using a new greedy based sorting mechanism. The simulated results reflect the efficiency of the proposed method in term of improved passband response along with better transition width. However, small reduction in As is observed within the allowable limit, when compared to non-fractional design approach, but the designed filter remains immune to wordlength &#x0028 WL &#x0029 effect.

A Survey on Filtering Issues for Two-Dimensional Systems: Advances and Challenges

In the past few decades, both theoretical developments and practical applications of the two-dimensional (2-D) systems have received considerable research interest due to their clear engineering insights in many industrial branches. In order to estimate the actual system states from available and possibly noisy measurements, various filtering strategies have been proposed in the existing literature which include, but are not limited to, Kalman filtering, variance-constrained filtering, H∞ filtering, l2-l∞ filtering, l1 filtering, dissipative filtering, and protocol-based filtering approaches. In particular, the filtering issues of 2-D systems subjected to disturbances/noises from different sources have drawn much research attention. The intent of this survey is to not only provide a comprehensive review of the latest results but also bestow some in-depth insights on the future research with respect to the filter design problems for 2-D systems under various performance requirements. The fundamentals of the 2-D systems are first presented and the corresponding filtering techniques are introduced from the aspects of engineering insights and physical interpretations. Then, several well-known 2-D systems are recalled and discussed, and some other 2-D systems with more complicated dynamics are surveyed. Subsequently, the recent advances in designing appropriate 2-D filtering algorithms under specific filtering performance indices are reviewed. Finally, this paper is concluded with some possible future research topics outlined on the 2-D filtering schemes.

Reliable filter design for discrete-time neural networks with Markovian jumping parameters and time-varying delay

This paper considers the problem of reliable filter design for discrete-time neural networks subject to Markovian jumping parameters and time-varying delay. Firstly, based on a matrix inequality, a new sufficient condition, which guarantees the existence of a reliable filter such that the resulting filtering error system is stochastically stable and extended dissipative, is established. Second, a less conservative stability criterion for neural networks is proposed. Then, the condition for the solvability of the filter design problem is given in terms of linear matrix inequalities (LMIs). Finally, three numerical examples are given to illustrate the effectiveness and advantages of the proposed filter design scheme.

A prototype filter design based on channel estimation for OQAM/OFDM systems

A prototype filter design for offset quadrature amplitude–modulation based orthogonal frequency division multiplexing (OQAM/OFDM) systems is proposed in this study. The influence of channel estimation is considered, with the object to minimize stop-band energy. An efficient preamble structure is proposed to improve the channel estimation and save the frequency spectral efficiency. The reciprocal of the signal-to-interference-plus-noise ratio (RSINR) is derived to measure the influence of the prototype for channel estimation. After that, the problem of prototype filter design is formulated as an optimization problem with constraints on the RSINR. To accelerate the convergence and obtain a global optimal solution, the box-based branch and bound algorithm is utilized to solve the optimization problem. Simulation results demonstrate the validity and efficiency of the prototype filter designed in this study.

Distributed filtering for a class of discrete-time systems over wireless sensor networks

Abstract This paper addresses the distributed filter design problem for a class of dynamic systems over wireless sensor networks. The missing measurements and the correlation among state noises and measurement noises are considered, where a set of mutually uncorrelated random variables is employed to describe the missing phenomena. Firstly, the construction of the designed filter is proposed and the prediction of the state at each node is given. Then, the filtering error covariance is presented and the filter parameters are determined to minimize the trace of such a covariance, where the network topology data are used to simplified the singular matrix. Subsequently, the relationship between the filter performance and missing probability of the measurement is discussed. Finally, a numerical simulation is presented to illustrate the effectiveness and capability of the proposed distributed filters.

Fault Detection Filter Design of Polytopic Uncertain Continuous-Time Singular Markovian Jump Systems with Time-Varying Delays

This paper investigates the problem of full-order and reduced-order fault detection filter (FDF) design under unified linear matrix inequality (LMI) conditions for a class of continuous-time singular Markovian jump systems (CTSMJSs) with time-varying delays and polytopic uncertain transition rates. By constructing a new Lyapunov function, sufficient conditions are firstly provided for the singular model error augmented system such that the system is stochastically admissible with an H∞ performance level γ. And then, by applying a novel convex polyhedron technique to decoupled linear matrix inequalities, the full-order and reduced-order fault detection filter parameters can be obtained within a convex optimization frame. The reduced-order fault detection filter (FDF) can not only meet the fault detection accuracy requirements of complex systems but also improve the fault detection efficiency. Finally, a DC motor and an illustrative simulation example are given to verify the feasibility and effectiveness of the proposed algorithms.

Fault estimation for fractional-order linear systems with polytopic uncertainties in the finite frequency domain

This paper investigates the problem of fault estimation for fractional-order systems with polytopic uncertainties. The faults and disturbances are considered to belong to finite frequency domains, and a fractional-order robust fault estimation filter is proposed for fault estimation with the known frequency ranges. As applications of the generalized Kalman–Yakubovich–Popov lemma, the fault estimation filter design problem is transformed into a multi-objective optimisation problem. By using the projection lemma and a linearising change of variables, sufficient conditions for the existence of the robust fault estimator are derived on the basis of a set of linear matrix inequalities. Numerical examples are used to demonstrate the validity of the presented method.

Dissipative-based non-fragile filtering for fuzzy networked control systems with switching communication channels

The problem of finite-time dissipative-based non-fragile filter design for a class of discrete-time nonlinear networked control systems subject to randomly occurring parameter uncertainties, external disturbances as well as packet dropouts due to unreliable communication channels and networked induced time-varying delay is discussed. The randomly occurring uncertainties are assumed to satisfy Bernoulli distribution and the considered stochastic variables are mutually independent. Multiple communication channel scenario is taken into account to enhance the data transmission performance and the channel switching phenomena is governed by a Markov chain. The intention here is to design a dissipative-based non-fragile filter for the filtering error system such that it is stochastically finite-time bounded and attains a prescribed dissipative performance attenuation level. Lyapunov stability theory and linear matrix inequality approach are exploited to develop a set of sufficient criterion for the existence of the desired filter. Finally, the filter design framework is applied to the truck-trailer model to demonstrate its effectiveness.

Sistem Deteksi Biometrik Keunikan Wajah Secara Real Time

<table width="605" border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="382"><p>Facial recognition is important for identifying a person's biodata profile. The physical development of students from the time they entered college to graduation has experienced inconspicuous changes but it is sometimes difficult to identify faces one by one. Digital form is becoming a trend to remember more real time. An important part of human physical identification has begun to shift from signature - finger - face selection. The face includes five important senses that are interconnected into an identification device. In this study the focus is on face detection based on color, the application of the Camshift Algorithm and finding the distance between the face sensing points is the result of the Gabor Wavelet method. Training data uses 4-8 second real time video. The hue histogram is basically the same as the RGB histogram, the difference is that the hue histogram uses the Hue value instead of RGB because the hue value represents natural color without regard to lighting. Gabor Wavelet transform is provided to solve filter design problems. The face detection system looks for face points to form a frame-shaped face selection if previously the face has been stored in a database so the system can easily describe biodata. Face selection can be done on live testing data. The selection box detection follows every facial movement.</p></td></tr></tbody></table>