IIR System Research Articles

Estimation of IIR Systems with Binary-Valued Observations

Estimation of IIR Systems with Binary-Valued Observations

Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Golden jackal optimization (GJO) is inspired by the cooperative attacking behavior of golden jackals and mainly simulates searching for prey, stalking and enclosing prey, and pouncing on prey to solve complicated optimization problems. However, the basic GJO has the disadvantages of premature convergence, a slow convergence rate and low computation precision. To enhance the overall search and optimization abilities, an enhanced golden jackal optimization (EGJO) method with the elite opposition-based learning technique and the simplex technique is proposed to address adaptive infinite impulse response system identification. The intention is to minimize the error fitness value and obtain the appropriate control parameters. The elite opposition-based learning technique boosts population diversity, enhances the exploration ability, extends the search range and avoids search stagnation. The simplex technique accelerates the search process, enhances the exploitation ability, improves the computational precision and increases the optimization depth. EGJO can not only achieve complementary advantages to avoid search stagnation but also balance exploration and exploitation to arrive at the best value. Three sets of experiments are used to verify the effectiveness and feasibility of EGJO. The experimental results clearly demonstrate that the optimization efficiency and recognition accuracy of EGJO are superior to those of AOA, GTO, HHO, MDWA, RSO, WOA, TSA and GJO. EGJO has a faster convergence rate, higher computation precision, better control parameters and better fitness value, and it is stable and resilient in solving the IIR system identification problem.

A Whale Optimization Algorithm with Convergence and Exploitability Enhancement and Its Application

The whale optimization algorithm (WOA) is inspired by humpback whale social behavior and WOA is a popular swarm intelligence algorithm. Yet, the WOA does have certain flaws, such as a restricted global search capability and an inconsistent convergence speed, and when dealing with complex optimization problems, WOA is easy to fall into local optimum. To address the WOA’s shortcomings, a modified whale optimization algorithm with convergence and exploitability enhancement called MWOA-CEE is proposed. Three operators, opposition-based learning, exponentially decreasing function, and elite-guided Cauchy mutation, are integrated into the WOA to enhance the exploration performance and avoid local optimum. First, the opposition-based learning used the opposition-based concept to generate the opposite position of the candidate solution, which can enhance the convergence ability and quality of the solution. Then, the exponentially decreasing function balances the exploitation and exploration phases and boosts the exploitation capability. In the end, the elite-guided Cauchy mutation generated a wide disturbance near the current candidate solution, which improves solutions diversity and increases the algorithm’s global search capability. MWOA-CEE is validated on 19 optimization benchmark functions and 14 CEC2014 benchmark functions and used to identify IIR systems. It is compared to seven of the commonly used algorithms. The results prove that MWOA-CEE outperforms the other algorithms in terms of final solution quality and convergence rate.

Inclined planes system optimization: Theory, literature review, and state-of-the-art versions for IIR system identification

The Inclined Planes System Optimization (IPO) algorithm is recent algorithm that uses Newton’s second law to perform optimization. After conducting a thorough literature review, this paper proposes an improved version of IPO called IIPO. This improvement is achieved by changing exploratory and exploitative behavior of the standard IPO proportional to the progress of optimization (iteration). The IIPO is employed for optimizing IIR digital filter design, which is a challenging engineering problem. Adaptive IIR modeling as a multimodal optimization problem is designed and developed under system identification structure with an appropriate single-objective function in the frequency domain. Implementations are performed in both modeling cases with same and reduced orders, and under two identification forms with and without environmental additive noise. The results are reported along with various analyzes compared to a wide range of IPO variants. The statistical results on 100 independent trials show a success of more than 90% of cases, the proposed IIPO algorithm substantially outperforms other comparative algorithms in terms of accuracy of estimated coefficients, convergence, fitness, output responses, noise analysis, stability, and reliability.11All source codes are fully and publicly available at https://github.com/ali-ece.

Dynamic opposite learning enhanced artificial ecosystem optimizer for IIR system identification

Dynamic opposite learning enhanced artificial ecosystem optimizer for IIR system identification

Recursive identification of IIR system using binary input/output measurements

Recursive identification of IIR system using binary input/output measurements

Comparison of algorithms for identification of IIR systems from binary measurements on the output

In the last decade, a significant number of algorithms of systems using only a quantized output, has appeared. In this study, an overview of identification methods of IIR systems, using quantized or binary output, is presented. A short description of the methods existing in the literature is given. The methods are compared both on the basis of simulation examples.

Adaptive Parameter Estimation of IIR System-Based WSN Using Multihop Diffusion in Distributed Approach

Distributed estimation of parameters in wireless sensor networks is taken into consideration to reduce the communication overhead of the network which makes the sensor system energy efficient. Most of the distributed approaches in literature, the sensor system is modeled with finite impulse response as it is inherently stable. Whereas in real time applications of WSN like target tracking, fast rerouting requires, infinite impulse response system (IIR) is used to model and that has been chosen in this work. It is assumed that every sensor node is equipped with IIR adaptive system. The diffusion least mean square (DLMS) algorithm is used to estimate the parameters of the IIR system where each node in the network cooperates themselves. In a sparse WSN, the performance of a DLMS algorithm reduces as the degree of the node decreases. In order to increase the estimation accuracy with a smaller number of iterations, the sensor node needs to share their information with more neighbors. This is feasible by communicating each node with multi-hop nodes instead of one-hop only. Therefore the parameters of an IIR system is estimated in distributed sparse sensor network using multihop diffusion LMS algorithm. The simulation results exhibit superior performance of the multihop diffusion LMS over non-cooperative and conventional diffusion algorithms.

Adolescent Identity Search Algorithm (AISA): A novel metaheuristic approach for solving optimization problems

This paper proposes a novel population-based metaheuristic optimization algorithm, called Adolescent Identity Search Algorithm (AISA), which is inspired by the process of identity development/search of adolescents. AISA simulates the identity formation behavior of adolescents in the peer group. This behavior is modeled mathematically to solve optimization problems. The proposed algorithm is evaluated on thirty-nine well-known unimodal, multimodal, fixed-dimensional multimodal, composite and CEC 2019 benchmark functions to test exploration, exploitation, local optima avoidance, and convergence properties. The results are verified by an extensive comparative study with thirteen state-of-art metaheuristic algorithms. Furthermore, AISA has been used to solve IIR system identification and inverse kinematics problem of a seven Degrees Of Freedom (7-DOF) robot manipulator considered as the real-life engineering applications. The overall optimization results demonstrate that AISA possesses a strong and robust capability to produce superior performance over other competitor metaheuristic algorithms in solving various complex numerical optimization problems.

A smart metaheuristic algorithm for solving engineering problems

Every day, the immature sunflowers generate heliotropic movements. Two growth mechanisms are controlled on the heliotropic movements of immature sunflowers. The first mechanism is the sun-tracking phenomenon, which is caused by a growth hormone called Auxin.While the second mechanism is the biological clock which is responsible for the nocturnal reorientation of the immature sunflowers at the night. By clarifying and idealizing the growth mechanisms of the immature sunflowers in nature, a new type of nature-inspired optimization algorithm, called Smart Flower Optimization Algorithm (SFOA), is proposed in this paper. The proposed algorithm has been presented in two modes: sunny and cloudy or rainy modes depending on the weather. The SFOA is benchmarked on three parts. First, a set of 15 benchmark functions on CEC 2015 is used to test the efficiency of the SFOA using statistical analysis and Wilcoxon’s test. Second, the SFOA is utilized for designing an adaptive IIR system to appropriate the unknown system. Finally, four different engineering design problems (three-bar truss, tension/compression spring, speed reducer, and welded beam) are solved by SFOA. In the first part, the SFOA is compared with well-known algorithms. The results confirm the capabilities and efficiency of the proposed algorithm in finding the optimum. The promising solutions obtained for the IIR system identification and the engineering design problems demonstrate that the SFOA significantly outperforms other algorithms and show the applicability of the proposed algorithm in solving the real-world problems with unknown search spaces as well.

The effects of perceived chronic pressure and time constraint on information search behaviors and experience

In this paper, we explore the effects of individual pressure level and time constraint on searchers' behaviors and their assessment of search experience within the framework of interactive information retrieval. A user experiment was conducted in which 40 participants individually searched for information in a laboratory setting under two conditions: with time constraint (TC) and with no time constraint (NTC). Participants filled in a Perceived Stress Scale questionnaire to measure their chronic pressure value (subjective stress), and their pressure value was recorded as their individual characteristic. The results showed that the more chronic pressure the searcher has, the more search efforts they devote, including more time in searching and more time to complete the search tasks, especially when there was no time constraint. Time constraint and searchers’ pressure value had a significant effect on users’ numbers of scrolling actions per minute. The results indicate that when given a time constraint, searchers with higher-pressure values tend to lower their reading or scanning speed, while searchers with lower-pressure values tend to accelerate their reading or scanning speed. The results suggested different people would react to the time condition change in different ways, especially people with higher pressure. Therefore, it is necessary to examine users’ search behaviors in person-in-situation frameworks to analyze the effects of contextual factors on users. This study contributes to our knowledge of how contextual factors and individual characteristics affect searchers’ behaviors and have implications for the design of IIR systems.

Comparing and evaluating an interactive, subject-based search system with the traditional digital library’s search functionality

Purpose-The purpose of this paper is to compare and evaluate the usability, usefulness and effectiveness of an Interactive, Information Retrieval – IIR system with a DSpace-based digital library.Design/methodology/approach– The proposed evaluation approach consists of two subcomponents. The first one refers to a log file analysis capable of revealing quantitative features of the systems’ usage. The second part refers to a user survey that compares the new IIR system against the traditional subject-based search functionality provided by DSpace in terms of usefulness and effectiveness. Findings- Based on the evaluation results, it seems that users are very interested in employing new methods and techniques in information seeking and retrieval, especially when such new tools and methods help them in fulfilling their information needs accurately and timely. The results also revealed that the users are more satisfied when employing the new search functionality and the search and retrieval process is improved.Originality/value - A novel IIR system for subject-based browsing was evaluated and interesting results for the future of such tools are shown.

A new design method for adaptive IIR system identification using hybrid CPSO and DE

Adaptive infinite impulse response filters have received much attention due to its utilization in a wide range of real-world applications. The design of the IIR filters poses a typically nonlinear, non-differentiable and multimodal problem in the estimation of the coefficient parameters. The aim of the current study is the application of a novel hybrid optimization technique based on the combination of cellular particle swarm optimization and differential evolution called CPSO–DE for the optimal parameter estimation of IIR filters. DE is used as the evolution rule of the cellular part in CPSO to improve the performance of the original CPSO. Benchmark IIR systems commonly used in the specialized literature have been selected for tuning the parameters and demonstrating the effectiveness of the CPSO–DE method. The proposed CPSO–DE method is experimentally compared with two new design methods: the tissue-like membrane system (TMS), the hybrid particle swarm optimization and gravitational search algorithm (HPSO–GSA), the original CPSO-outer and CPSO-inner, and classical implementations of PSO, GSA and DE. Computational results and comparison of CPSO–DE with the other evolutionary and hybrid methods show satisfactory results. The hybridization of CPSO and DE demonstrates powerful estimation ability. In particular, to our knowledge, this hybridization has not yet been investigated for the IIR system identification.

An Incremental RLS for Distributed Parameter Estimation of IIR Systems Present in Computing Nodes of a Wireless Sensor Network

Two popular learning algorithms namely incremental least mean square (ILMS) and diffusion least mean square (DLMS) have already been reported in literature for distributed parameter estimation using the data collected from sensor nodes. The ILMS has long convergence problem for IIR systems. The DLMS has more communication overhead which leads to excess computational complexity. To alleviate this problem in this paper a fast incremental recursive least mean square algorithm (IRLS) is proposed for IIR systems present in the computing nodes of a wireless sensor network. Simulation results demonstrate that proposed algorithm provide faster convergence and accurate performance in two examples.

FIR–IIR adaptive filters hybrid combination

To enhance the performance in IIR system identification scenarios, a hybrid combination of FIR and IIR adaptive filters (AFs) via a supervisor that senses which one is performing best is proposed. The FIR-LMS AF is short, providing fast and robust convergence, whereas the IIR-LMS AF is slow but accurate. The stagnation effect caused by the different convergence rates is tackled through cyclic weight transfers FIR → IIR, which also ensure good tracking properties. A design technique for the transfers cycle length is proposed, providing good convergence while keeping computational cost low.

Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization

In recent years because of substantial use of wireless sensor network the distributed estimation has attracted the attention of many researchers. Two popular learning algorithms: incremental least mean square (ILMS) and diffusion least mean square (DLMS) have been reported for distributed estimation using the data collected from sensor nodes. But these algorithms, being derivative based, have a tendency of providing local minima solution particularly for minimization of multimodal cost function. Hence for problems like distributed parameters estimation of IIR systems, alternative distributed algorithms are required to be developed. Keeping this in view the present paper proposes two population based incremental particle swarm optimization (IPSO) algorithms for estimation of parameters of noisy IIR systems. But the proposed IPSO algorithms provide poor performance when the measured data is contaminated with outliers in the training samples. To alleviate this problem the paper has proposed a robust distributed algorithm (RDIPSO) for IIR system identification task. The simulation results of benchmark IIR systems demonstrate that the proposed algorithms provide excellent identification performance in all cases even when the training samples are contaminated with outliers.

Identification of Wiener Systems With Clipped Observations

In this paper, we consider the parametric version of Wiener systems where both the linear and nonlinear parts are identified with clipped observations in the presence of internal and external noises. Also the static functions are allowed noninvertible. We propose a classification based support vector machine (SVM) and formulate the identification problem as a convex optimization. The solution to the optimization problem converges to the true parameters of the linear system if it is an finite-impulse-response (FIR) system, even though clipping reduces a great deal of information about the system characteristics. In identifying a Wiener system with a stable infinite-impulse-response (IIR) system, an FIR system is used to approximate it and the problem is converted to identifying the FIR system together with solving a set of nonlinear equations. This leads to biased estimates of parameters in the IIR system while the bias can be controlled by choosing the order of the approximated FIR system.

Blind identification of multi-channel systems with single input and unknown orders

In commonly used SOS-based methods for blind identification of single input multiple output (SIMO) systems, such as subspace (SS) and least squares (LS), the highest order of unknown FIR channels is assumed to be known or is overestimated. In this paper we propose a new method based on modified Levinson algorithm (MLA) that makes no such assumptions. This method determines which FIR channel is minimum-phase, and estimates every channel impulse response with correct order when at least one of the unknown FIR channels is invertible. We first develop a MLA-based method for estimating a LTI SISO stable IIR system of correct orders when the input is an arbitrary stationary signal. We then use the results of such method and propose a solution to the SIMO blind system identification problem when orders of FIR channels are unknown. Performance of the proposed method is compared with those of the SS and power of R (POR) methods using different signal to noise ratio.

Boundedness of Multi-dimensional Systems over a Prescribed Frequency Domain

On the basis of a parameterization of the span of a multivariate matrix polynomial, a sufficient condition is derived in this paper for a multi-dimensional multi-input multi-output (MIMO) IIR system being upper bounded over a cuboid frequency domain. This condition is expressed through a linear matrix inequality (LMI) and can be computationally verified. Moreover, by means of parameter dependent LMIs, two necessary and sufficient conditions are also obtained for this boundedness verification problem, which are again expressed by LMIs. Furthermore, LMI based conditions are derived for system output matrix and direct transmission matrix. Numerical examples are included to illustrate the efficiency and characteristics of the derived theoretical results.

Self-Tuning Blind Identification and Equalization of IIR Channels

This paper considers self-tuning blind identification and equalization of fractionally spaced IIR channels. One recursive estimator is used to generate parameter estimates of the numerators of IIR systems, while the other estimates denominator of IIR channel. Equalizer parameters are calculated by solving Bezout type equation. It is shown that the numerator parameter estimates converge (a.s.) toward a scalar multiple of the true coefficients, while the second algorithm provides consistent denominator estimates. It is proved that the equalizer output converges (a.s.) to a scalar version of the actual symbol sequence.