Infinite Impulse Response System Research Articles

Pattern Search Ameliorated Arithmetic Optimization Algorithm for Engineering Optimization and Infinite Impulse Response System Identification

Pattern Search Ameliorated Arithmetic Optimization Algorithm for Engineering Optimization and Infinite Impulse Response System Identification

Reduced order infinite impulse response system identification using manta ray foraging optimization

This article presents a useful application of the Manta Ray Foraging Optimization (MRFO) algorithm for solving the adaptive infinite impulse response (IIR) system identification problem. The effectiveness of the proposed technique is validated on four benchmark IIR models for reduced order system identification. The stability of the proposed estimated IIR system is assured by incorporating a pole-finding and initialization routine in the search procedure of the MRFO algorithm and this algorithmic modification contributes to the MRFO algorithm when seeking stable IIR filter solutions. The absence of such a scheme, which is primarily the case with the majority of the recently published literature, may lead to the generation of an unstable IIR filter for unknown real-world instances (particularly when the estimation order increases). Experiments conducted in this study highlight that the proposed technique helps to achieve a stable filter even though large bounds for the design variables are considered. The convergence rate, robustness, and computational speed of MRFO for all the considered problems are investigated. The influence of the control parameters of MRFO on the design performances is evaluated to gain insight into the interaction between the three foraging strategies of the algorithm. Extensive statistical performance analyses employing various non-parametric hypothesis tests concerning the design consistency and convergence are conducted for comparison of the proposed MRFO-based approach with six other metaheuristic search procedures to investigate the efficiency. The results on the mean square error metric also highlight the improved solution quality of the proposed approach compared to the various techniques published in the literature.

An atom search optimization approach for IIR system identification

ABSTRACT Filtering, or digital signal processing, is a significant and fundamental requirement in fields such as signal systems and computers. The process of designing optimal digital filters is difficult, which has led researchers to design filters using emerging evolutionary computations. Metaheuristics have emerged as the most promising tool for solving optimization problems, with excellent development and improvement. However, it has not been clear how to select the best performing metaheuristic to design an optimal digital filter. In this paper, a digital infinite impulse response (IIR) filter is constructed using the atom search optimization (ASO) algorithm impressed by the physical motion of atoms in nature based on molecular dynamics. The simulation results obtained are extensively compared with the results of other optimization algorithms such as moth flame optimization, gravitational search algorithm and artificial bee colony optimization. ASO was found to have the highest percentage of improvement. Furthermore, eight cases are analyzed across four numerical filter instances with the same degree and four with reduced degree, and the results are validated by outperforming several different algorithm-based approaches in the literature. The stability analysis on the basis of pole zero diagrams further cements the efficacy of the ASO for IIR system identification problem.

An improved artificial rabbits optimization for accurate and efficient infinite impulse response system identification

Identifying models with Infinite Impulse Response (IIR) is crucial in signal processing and system identification. This paper addresses the challenges of IIR model identification by proposing an improved version of the Artificial Rabbits Optimization (ARO) algorithm called improved ARO (IARO). The IARO algorithm integrates an adaptive local search mechanism and an experience-based perturbed learning strategy as two key enhancements to improve the effectiveness of ARO. These additions aim to address the loss of accuracy during iterations and improve the algorithm’s ability to exploit promising areas. Four benchmark examples of different IIR plants are considered, and the performance of the proposed IARO algorithm is compared to existing competitive methods. The results consistently demonstrate that IARO outperforms ARO in accuracy and convergence for system identification across all orders of IIR systems. Visual analysis, convergence curves, coefficient comparison, and statistical metrics comparison all validate the superiority of the IARO algorithm. Additionally, the Wilcoxon signed-rank test results provide further statistical evidence supporting the superior performance of IARO. The comprehensive analysis showcases the efficacy and effectiveness of the IARO algorithm in accurately identifying IIR systems. This work represents a significant advancement in IIR system identification, offering a superior methodology for accurate and efficient system modeling.

Deep convolution neural network based Parkinson’s disease detection using line spectral frequency spectrum of running speech

The deformation of speech caused by glottic vocal tract is an early bio marker for Parkinson’s disease. A novel idea of Line Spectral Frequency trajectory spectrum image representation of the speech signals of the subjects in Deep Convolution Neural Network is proposed for Parkinson’s disease classification in which the convolution layer automatically learn the features from the input images and no separate feature calculation stage in required. The human vocal tract that produces a short phonetics is assumed as an all-pole Infinite impulse response system and the Line spectral frequency trajectory spectrum images represents the poles of the system and reflects the voice defects due to Parkinson’s disease. It is shown that the proposed method outperforms the existing state of the art work for two different utterance tasks one for sustained phonation and another for natural running speech dataset. It is demonstrated that the Deep Convolution Neural Network results in a training accuracy of 92.5% for sustained phonation dataset and training accuracy of 99.18% for King’s college running speech dataset. The validation accuracies for both the datasets are 100%. The proposed work is much better than another recent benchmark work in which Mel Frequency Cepstral Coefficient parameters are used in machine learning for Parkinson’s disease detection in running speech. The high performance of the proposed method for King’s college running speech dataset which is collected through mobile device voice recordings, gains attention. Rigorous performance analysis is performed for running speech dataset by using separate isolated test set for repeated 50 trials and the performance metrics are F1 score of 99.37%, sensitivity of 100%, precision of 98.75% and specificity of 99.27%.

Enhancing IIR system identification: Harnessing the synergy of gazelle optimization and simulated annealing algorithms

In the field of digital filter design and system identification, accurately modeling Infinite Impulse Response (IIR) systems is of utmost importance. This paper introduces a new adaptive algorithm that combines the gazelle optimization algorithm with simulated annealing to achieve superior optimization performance for digital IIR filters used in system identification. To evaluate the algorithm's effectiveness, extensive experiments were conducted on two fronts: the challenging CEC2020 benchmark functions and a fifth order IIR system identification problem. The proposed algorithm proves its effectiveness by achieving optimal solutions on the CEC2020 benchmark functions when compared to other well-known optimization algorithms such as arithmetic optimization, particle swarm optimization, differential evolution, sine cosine algorithm, gray wolf optimizer, biogeography-based optimization, salp swarm algorithm, and the original gazelle optimization algorithm. The algorithm's performance is measured by its ability to find the best solutions for these benchmark functions. For the IIR system identification problem, the study considers both the same order and reduced order cases. The system to be identified is represented by a fifth order plant. The algorithm's statistical analysis and convergence behavior are compared to the original gazelle optimization algorithm, artificial hummingbird algorithm, mountain gazelle optimization algorithm, and the widely used artificial bee colony algorithm. These comparisons demonstrate the proposed algorithm's superior capability and efficiency in accurately identifying the parameters of IIR systems.

Adaptive Infinite Impulse Response System Identification Using Elitist Teaching-Learning- Based Optimization Algorithm

Infinite Impulse Response (IIR) systems identification is complicated by traditional learning approaches. When reduced-order adaptive models are utilised for such identification, the performance suffers dramatically. The IIR system is identified as an optimization issue in this study. For system identification challenges, a novel population-based technique known as Elitist teacher learner-based optimization (ETLBO) is used to calculate the best coefficients of unknown infinite impulse response (IIR) systems. The MSE function is minimised and the optimal coefficients of an unknown IIR system are found in the system identification problem. The MSE is the difference between an adaptive IIR system's outputs and an unknown IIR system's outputs. For the unknown system coefficients of the same order and decreased order cases, exhaustive simulations have been performed. In terms of mean square error, convergence speed, and coefficient estimation, the results of actual and reduced-order identification for the standard system using the novel method outperform state-of-the-art techniques. For approximating the same-order and reduced-order IIR systems, four benchmark functions are examined utilizing GA, PSO, CSO, and BA. To demonstrate the improvements, the approach is evaluated on three conventional IIR systems of 2nd, 3rd, and 4th order models. On the basis of computing the mean square error (MSE) and fitness function, the suggested ETLBO approach for system identification is proven to be the best among others. Furthermore, it is confirmed that the suggested ETLBO method outperforms some of the other known system identification strategies. Finally, the efficiency of the dynamic nature of the control parameters of DE, TLBO, and BA in finding near parameter values of unknown systems is demonstrated through comparison data. The simulation results show that the suggested system identification approach outperforms the current methods for system identification.

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.

Simulated Annealing Aided Artificial Hummingbird Optimizer for Infinite Impulse Response System Identification

Simulated Annealing Aided Artificial Hummingbird Optimizer for Infinite Impulse Response System Identification

Application of Slime Mould Algorithm to Infinite Impulse Response System Identification Problem

Recently, the researchers working in the field of science and engineering have paid a considerable attention to the concept of the system identification to tackle with complex optimization problems. It is feasible to achieve more accurate models of physical plants with the infinite impulse response (IIR) models compared to their finite counterparts (FIR). To get the most out of the IIR models for the system identification, metaheuristic optimization algorithms can be used as efficient solutions. This work, therefore, aims to demonstrate more promising performance of a new metaheuristic algorithm named slime mould algorithm. In this regard, a comparative assessment is performed using different metaheuristic optimization techniques and different IIR model identification problems are considered. The slime mould algorithm is shown to achieve better accuracy and robustness in terms of IIR model identification with the help of obtained statistical results.

Recursive identification of IIR system using binary input/output measurements

The identification of system using binary measurements of both the input and output is a problem relevant to a number of applications. Although the identification of finite impulse response (FIR) models has been thorough, more complex model structures still need to be investigated. In this paper, the problem of identifying an infinite impulse response (IIR) system is studied. A three-step real-time algorithm is proposed. The proposed algorithm is based on the use of least mean squares (LMS) algorithm to estimate the correlation function, then estimate the parameters of the system. A convergence analysis of the proposed algorithm based on those of LMS algorithm is presented. Some simulation results are included to illustrate the performance of the proposed algorithm.

Infinite impulse response system identification using average differential evolution algorithm with local search

Infinite impulse response system identification using average differential evolution algorithm with local search

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.

Identification Using Binary Measurements for IIR Systems

This paper studies the identification of infinite impulse response (IIR) systems with binary-valued measurements. The key to our approach is to recast the initial identification problem into an identification problem in the set-membership framework. This enables us to identify the system using an identification algorithm devoted to IIR systems in such a framework. Although the proposed algorithm is designed for noise free data, an analysis is provided in the presence of noise, and illustrative examples are provided to demonstrate the effectiveness of the algorithm.

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

Infinite impulse response (IIR) system identification problem is defined as an IIR filter modeling to represent an unknown system. During a modeling task, unknown system parameters are estimated by metaheuristic algorithms through the IIR filter. This work deals with the self-adaptive search-equation-based artificial bee colony (SSEABC) algorithm that is adapted to optimal IIR filter design. SSEABC algorithm is a recent and improved variant of artificial bee colony (ABC) algorithm in which appropriate search equation is determined with a self-adaptive strategy. Moreover, the success of the SSEABC algorithm enhanced with a competitive local search selection strategy was proved on benchmark functions in our previous studies. The SSEABC algorithm is utilized in filter modelings which have different cases. In order to demonstrate the performance of the SSEABC algorithm on IIR filter design, we have also used canonical ABC, modified ABC (MABC), best neighbor-guided ABC, and an ABC with an adaptive population size (APABC) algorithms as well as other algorithms in the literature for comparison. The obtained results and the analysis on performance evolution of compared algorithms on several filter design cases indicate that SSEABC outperforms all considered ABC variants and other algorithms in the literature.

Synthesis and Realization of Two-Dimensional Separable Denominator Orthogonal Systems via Decomposition Into 1-D Systems

This paper presents a realization and implements techniques of 2-D linear time-invariant discrete filters described by a system function. Two classes are covered: finite- and infinite-impulse response systems with a separable denominator. This realization is based on a state-space approach, known as orthogonal filters, resulting in structures consisting of Givens rotations and delays. The techniques that are presented are illustrated by an edge detection system and a separable denominator band-pass filter designs. For the designed structures, magnitude errors caused by a finite precision arithmetic have been evaluated and compared with those obtained for direct structures based on difference equations. It has been shown that orthogonal structures possess lower magnitude errors.

Improved Self-adaptive Search Equation-based Artificial Bee Colony Algorithm with competitive local search strategy

Abstract The search equations and the local search procedures used in Artificial Bee Colony (ABC) algorithm are two key components that affect the performance of the algorithm. However, there is no search equation or local search that provides good results for all problem types. In this article, an ABC algorithm called “Self-adaptive Search Equation-based Artificial Bee Colony” (SSEABC) is proposed which can determine the appropriate local search procedure and the search equation internally during execution. SSEABC integrates three strategies into the canonical ABC algorithm. The first strategy is a self-adaptive strategy that determines the appropriate search equations for a particular problem by eliminating improper ones from a pool consisting of randomly generated search equations. The second strategy is a competitive local search selection. It decides the most effective local search procedure by comparing the performances of SSEABC, Mtsls1 and IPOP-CMA-ES. The third strategy is an incremental population size strategy, which is based on adding new food sources located around the best-so-far food source position after a predefined number of iterations. This helps to increase convergence speed. The SSEABC algorithm is tested on benchmark functions proposed in the CEC'14 abd CEC'17 competition on single objective bound constrained real-parameter numerical optimization. SSEABC is compared with several ABC variants, competitor algorithms of CEC'14 and CEC'17, and several state-of-the-art algorithms. Finally, we applied SSEABC to the infinite impulse response (IIR) system identification problem as an engineering application. The results showed the superiority of the SSEABC algorithm.

Selfish herd optimization algorithm based on chaotic strategy for adaptive IIR system identification problem

The design method of adaptive infinite impulse response (IIR) filter is a challenging problem. Its design principle is to determine the filter parameters by the iteration process of the adaptive algorithm, which is to obtain an optimal model for unknown plant based on minimizing mean square error (MSE). However, many adaptive algorithms cannot adjust the parameters of IIR filter to the minimum MSE. Therefore, a more efficient adaptive optimization algorithm is required to adjust the parameters of IIR filter. In this paper, we propose a selfish herd optimization algorithm based on chaotic strategy (CSHO) and apply it to solving IIR system identification problem. In CSHO, we add a chaotic search strategy, which is a better local optimization strategy. Its function is to search for better candidate solutions around the global optimal solution, which makes the local search of the algorithm more precise and finds out potential global optimal solutions. We use solving IIR system identification problem to verify the effectiveness of CSHO. Ten typical IIR filter models with the same order and reduced order are selected for experiments. The experimental results of CSHO compare with those of bat algorithm (BA), cellular particle swarm optimization and differential evolution (CPSO-DE), firefly algorithm (FFA), hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA), improved particle swarm optimization (IPSO) and opposition-based harmony search algorithm (OHS), respectively. The experimental results show that CSHO has better optimization accuracy, convergence speed and stability in solving most of the IIR system identification problems. At the same time, it also obtains better optimization parameters and achieves smaller difference between actual output and expected output in test samples.

Modified Whale Optimization Algorithm for Infinitive Impulse Response System Identification

System identification based on infinite impulse response (IIR) models has received much attention because it is used in a variety of real-world applications. However, an IIR model might have a multimodal error surface. To attain an ideal identification, an efficient and robust method is necessary. In this study, a modified whale optimization algorithm (WOA) with a ranking-based mutation operator, called the RWOA, is presented to solve the IIR system identification problem. The RWOA integrates a ranking-based mutation operator into the basic WOA to enhance performance by speeding up the convergence rate and then enhances the exploitation capability. The experimental results of actual and reduced-order identification for a standard system using our proposed RWOA were superior to those of five state-of-the-art algorithms (including the basic WOA), in terms of improving the quality and stability of the results, in most cases, and significantly speeding up convergence.

Adaptive infinite impulse response system identification using teacher learner based optimization algorithm

In this paper, optimal coefficients of unknown infinite impulse response (IIR) system are computed by utilizing a new population based algorithm called teacher learner based optimization (TLBO) for system identification problem. TLBO algorithm is inspired by the teaching learning process in the classroom and is free from algorithmic specific parameters. In TLBO, difference mean is calculated for each learner, which is the difference between the existing mean result of the class and the teacher. This difference mean is updated in each iteration and is responsible for maintaining the diversity of this algorithm. System identification problem is based on minimizing the mean square error (MSE) function and finding the optimal coefficients of an unknown IIR system. The MSE is the difference between the outputs of an adaptive IIR system and an unknown IIR system. Exhaustive simulations have been done for finding the unknown system coefficients of same order and reduced order case. Four benchmark functions are tested using TLBO algorithm to verify its efficacy for system identification problem. In order to prove the effectiveness of the applied algorithm, evaluated coefficients and MSE values are compared with that of the genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), firefly algorithm (FFA), bat algorithm (BAT), differential evolution with wavelet mutation (DEWM), harmony search (HS) and opposition based harmony search (OHS) algorithm.