Adaptive Infinite Impulse Response Research Articles

An Enhanced Symmetric Sand Cat Swarm Optimization with Multiple Strategies for Adaptive Infinite Impulse Response System Identification

An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter’s parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system’s input and the IIR filter’s output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value.

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.

Maximum Complex Correntropy Criterion Adaptive IIR Filtering Based on Gauss-Newton Approach

Adaptive infinite impulse response (IIR) filters have received noticeable consideration for its lower computational complexity compared with adaptive finite impulse response (FIR) filters recently. At present, the adaptive IIR algorithms are almost using the well-established stochastic gradient descent method, but the convergence rate is less than satisfactory. In this paper, to pursue the fast convergence speed of Gauss-Newton (GN) method, we present a GN algorithm for adaptive IIR filters with complex coefficients based on the complex correntropy measure. Besides, the steady-state excess mean square error is provided. Simulations are performed to show the eximious performance of this algorithm.

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.

LoEar

Acoustic sensing has been explored in numerous applications leveraging the wide deployment of acoustic-enabled devices. However, most of the existing acoustic sensing systems work in a very short range only due to fast attenuation of ultrasonic signals, hindering their real-world deployment. In this paper, we present a novel acoustic sensing system using only a single microphone and speaker, named LoEar, to detect vital signs (respiration and heartbeat) with a significantly increased sensing range. We first develop a model, namely Carrierforming, to enhance the signal-to-noise ratio (SNR) via coherent superposition across multiple subcarriers on the target path. We then propose a novel technique called Continuous-MUSIC (Continuous-MUltiple SIgnal Classification) to detect a dynamic reflections, containing subtle motion, and further identify the target user based on the frequency distribution to enable Carrierforming. Finally, we adopt an adaptive Infinite Impulse Response (IIR) comb notch filter to recover the heartbeat pattern from the Channel Frequency Response (CFR) measurements which are dominated by respiration and further develop a peak-based scheme to estimate respiration rate and heart rate. We conduct extensive experiments to evaluate our system, and results show that our system outperforms the state-of-the-art using commercial devices, i.e., the range of respiration sensing is increased from 2 m to 7 m, and the range of heartbeat sensing is increased from 1.2 m to 6.5 m.

Active Air-borne noise suppression for pipe break early warning in smart water networks

Acoustic alarm systems using IoT (internet of things) smart sensors (e.g. accelerometers) for leak detection in water networks are rapidly gaining popularity. One of the issues for such systems is the false alarms which often occur as a result of signal contamination by environmental noise, especially by air-borne noise resulting in additional vibration. This research proposes a dual-sensor concept for active air-borne noise suppression in accelerometer signals that are excited by both fluid-borne noise (including the leak signal) and air-borne environmental noise. In the proposed arrangement, a microphone adjacent to the measurement station is used to measure the air-borne noise simultaneously to the accelerometer mounted to the pipe. Three methods have been applied in this paper: 1) using the non-coherent output power (NCOP) between the accelerometer and microphone signals; 2) using a Wiener filter; and 3) using adaptive least mean square (LMS) filters together with adaptive infinite impulse response (IIR) filters. Field tests have been carried out on a cast iron water main embedded in a water distribution system. A speaker was used to generate air-borne noise around the measurement station and to excite unwanted vibrations in the pipe. The results show that all three methods can effectively suppress air-borne noise from the accelerometer signals mounted to the pipe. A noise reduction of 30 dB can be achieved using the NCOP in the frequency domain or using the Wiener filter in the time domain. The adaptive filters are relatively less effective and the effectiveness reduces with the increase in bandwidth of the air-borne noise. However, the adaptive filters have the potential to be applied online for real-time processing.

Single and Multiple Continuous-Wave Interference Suppression Using Adaptive IIR Notch Filters Based on Direct-Form Structure in a QPSK Communication System

The removal filter coefficients in this technique are dependent on the jammer’s power and its Instantaneous Frequency (IF) information, which can both be obtained in the time–frequency domain (adaptive filtering techniques). The dependence of the removing/reducing filter characteristics on the interference power is critical, as it allows an optimal trade-off between removal interference and the amount of self-noise generated by the filter. This trade-off is bounded by the two extreme cases of no notch filter (no self-noise) and full suppression (k1 = 1) for both low- and high-power jammer values. In this paper, a cascade second-order adaptive direct Infinite Impulse Response (IIR) Notch Filter (NF) with a gradient-based algorithm to suppress the Continuous-Wave (CW and MCW) interference is proposed for maximizing the receiver Signal-to-Noise Ratio (SNR) in a Quadrature Phase-Shift Keying (QPSK)-modulated signal. The suppression approach consists of two Adaptive IIR NFs (ANFs) based on a direct-form structure: the Hd1(z) and Hd1(z). The proposal in this work presents a low-complexity Time-Domain (TD) algorithm for controlling the update filter coefficient and notch depth. Simulation results demonstrate that the proposed approach represents an effective method for removing/reducing the impacts of CWI/MCWI, resulting in improved system performance for low- and high-power jammer values when compared with the case of full suppression (k1 = 1); furthermore, it also improves the notch filter’s output SNR for a given Jamming-to-Signal Ratio (JSR) value and Bit Error Ratio (BER) performance. For example, the SNR output of the proposed IIR NF was enhanced by 7 dB versus the case without a filter when Eb/No = 15 dB and JSR = −5 dB. The proposed method can detect and mitigate weak and strong jamming with JSR values ranging from −30 to 40 dB, and can track the hopping frequency interference. Moreover, an improved BER performance is seen as compared to the case without an IIR NF.

Convergence Rate For Low-Pass Infinite Impulse Response Digital Filter

The hybrid optimization technique is used to design a low-pass infinite impulse response (IIR) digital filter, to improve the coefficients of adaptive IIR digital filter, thus ensure stability. In this paper, the newly technique of hybrid particle swarm optimization (PSO) algorithm is proposed. This technique is a dynamic and static topology with a PSO algorithm, which called dynamic and static PSO (DS-PSO) algorithm. In the simulation, the low-pass IIR digital filter 8th order is designed. The fitness function problem is discussed based on values of the ripple of the passband, a ripple of the stopband, and a transition band. Thus, the proposed algorithm results are compared with previous studies results. From results comparison, results have shown the convergence rate with the DS-PSO algorithm outperformed the convergence rates using fuzzy gravitational search algorithm (FGSA), gravitational search algorithm (GSA), and differential evolution (DE) with mean values of 55.21%, 57.80%, and 70.40%, respectively.

Design and modeling of adaptive IIR filtering systems using a weighted sum - variable length particle swarm optimization

The use of optimization algorithms for designing Infinite Impulse Response (IIR) filters has been considered in many studies. The concern in this area is the multimodal error surface of such filters and their fitting with filter coefficients. The order of the modeled system has a direct effect on the number of coefficients, complexities of the error surface, and the filter’s stability. This paper proposes an efficient approach based on a variable length particle swarm optimization algorithm with a weighted sum fitness function (WS-VLPSO). The proposed WS-VLPSO is utilized as an effective adaptive algorithm for designing optimal IIR filters. The approach is based on the inclusion of the order as a discrete variable in the particle vector, which is done with the goal of intelligent minimizing of order and thereby reducing the design complexity of IIR filters. To ensure the optimality of the systems, the objective function is considered as a weighted sum. Also, a new criterion called Optimum Modeling Indicator (OMI) is introduced, a measure to determine the percentage reduction of order and the success rate of the proposed approach. The proposed algorithm is also applied for solving the sensor coverage problem as another real-world variable length engineering optimization application. Evaluation of simulation results, with Monte-Carlo simulation, indicates the acceptable improvement of identified structures and the significant performance of the proposed approaches. Note that the source codes of the paper will be publicly available at https://github.com/ali-ece.

A continuous-state cellular automata algorithm for global optimization

Cellular automata are capable of developing complex behaviors based on simple local interactions between their elements. Some of these characteristics have been used to propose and improve metaheuristics for global optimization; however, the properties offered by the evolution rules in cellular automata have not yet been used directly in optimization tasks. Inspired by the complexity that various evolution rules of cellular automata can offer, the continuous-state cellular automata algorithm is proposed. In this way, the algorithm takes advantage of different evolution rules to maintain a balance that maximizes the exploration and exploitation properties in each iteration. The efficiency of the algorithm is proven with 48 test problems widely used in the literature, 4 engineering applications that were also used in recent literature, and the design of adaptive infinite-impulse response filters, with the reference functions of 10 full-order filters being tested. The numerical results prove its competitiveness in comparison with state-of-the-art algorithms. The source codes of the proposed algorithm are publicly available at https://github.com/juanseck/CCAA.git.

Combined IIR and FIR Filter for Improved Power Quality of PV Interfaced Utility Grid

For enhancing the power quality (PQ), an adaptive infinite impulse response (IIR) combined with finite impulse response (FIR) control is utilized for a grid connected solar photovoltaic (PV) system. The power obtained from a solar PV array is affected due to the seasonal and diurnal variability of the sun. Moreover, the utility distribution network is relatively weak for the developing and underdeveloped countries, exacerbating PQ deterioration issues. For the improvement in PQ indices, an adaptive combined IIR and FIR filter is utilized here, which exhibits adaptive control capability of the voltage source converter essential for smart grid operations. The major advantage regarding the utilization of adaptive control involves much less sensitivity to quantization errors. This new application of a combined IIR and FIR filter includes the mitigation of harmonics in the grid currents and the grid voltages, thereby improving PQ along with reactive power compensation. Due to a combination of IIR and FIR filters, the filtering is achieved with fewer coefficients, requiring minimum memory (as in the case of IIR filters) along with easy implementation and design capability (in the case of FIR filters). Test cases corroborate satisfactory performance during the weak grid conditions, such as voltage distortion, unbalance, and swell, and experimental results are obtained though assessment on a developed laboratory prototype. The PQ improvement is evident by observing the total harmonics distortion conferring to the IEEE-519-2014 Std.

Development of equation-error adaptive IIR-filter-based active noise control system

An infinite impulse response (IIR) filter benefits to identify complex primary and secondary paths information with short filter length for active noise control (ANC) system. Conventional output-error (OE)-based system uses adaptive IIR filter with the filtered-U recursive least mean square (FURLMS) algorithm to cancel undesired noise. This paper develops an ANC system using equation-error (EE)-based IIR filter and analyzes its step size bound, global minimum and computation complexity. The EE-based ANC system can also solve measurement error problem for the error sensor. Several simulations show effectiveness and enhancement of the developed EE-based system.

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.

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.

IIR filter optimization using improved chaotic harmony search algorithm

Due to the fact that the error surface of adaptive infinite impulse response (IIR) systems is generally nonlinear and multimodal, conventional derivative-based techniques fail when used in adaptive Filter design. In this sense, global optimization techniques are required in order to avoid local minima. Harmony search (HS), a musical inspired metaheuristic, is a recently introduced population-based algorithm that has been successfully applied to global optimization problems. In the present paper, adaptive IIR filtering is formulated as a nonlinear optimization problem and then an improved version of HS incorporating chaotic search (CIHS) is introduced to solve the identification problem of three benchmark IIR systems. Furthermore, the performance of the proposed methodology is compared with HS and two well-known metaheuristic algorithms, genetic algorithm (GA) and particle swarm optimization (PSO) and a modified version of PSO called PSOW (Particle Swarm Optimization with weight Factor). The results demonstrate that the proposed method has superior performance over the other above-mentioned algorithms in terms of convergence speed and accuracy.

Diffusion augmented complex adaptive IIR algorithm for training widely linear ARMA models

In this paper, our aim is to propose a fully distributed adaptive algorithm for learning the parameters of a widely linear autoregressive moving average model by measurements collected by a network. To this end, we consider a connected network where every node uses the augmented complex adaptive infinite impulse response (ACA-IIR) filter as the learning rule. We firstly formulate the learning problem as an optimization problem and resort to stochastic gradient optimization argument to solve it and derive the proposed algorithm, which will be referred to as diffusion ACAIIR (DACA-IIR) algorithm. We also introduce a reduced-complexity version of the DACA-IIR algorithm. We use both synthetic and real-world signals in our simulations where the results show that the proposed cooperative algorithm outperforms the noncooperative solution.

Distributed parameter estimation of IIR system using diffusion particle swarm optimization algorithm

In wireless sensor networks (WSNs), distributed algorithms are used to estimate desired parameters for minimizing the communication overheads and make the network energy efficient. In literature, distributed estimation of finite impulse response (FIR) systems has been studied, because it is stable. In fact in many sensor network-based applications such as target tracking and fast rerouting, infinite impulse response (IIR) systems are required to be modeled. Thus, each sensor node uses the adaptive IIR filter and interact with each other under diffusion mode of cooperation to estimate the parameters. But the IIR filter inherently produces non-quadratic and multimodal error surfaces. Therefore gradient search algorithms that work well for FIR filters is not suitable for IIR system because they are likely to be trapped in the local minima. Keeping this in view, population based derivative free diffusion particle swarm optimization (DPSO) algorithms are proposed here to estimate the parameters of IIR systems. The algorithms are simulated for benchmark IIR systems and the steady state and transient performances are analyzed. The simulation results demonstrate that the proposed diffusion algorithms provide admirable improvement by resulting in faster convergence and low steady state value compared to that of conventional least mean squared algorithms.

Some new results of designing an IIR filter with colored noise for signal processing

The design of adaptive finite impulse response filters is a linear optimization problem and the design of adaptive infinite impulse response (IIR) filters in the presence of observation noise is a nonlinear optimization problem. This paper considers the parameter estimation issues of an infinite impulse response (IIR) filter with colored noise which is treated as an autoregressive process. The key is to investigate novel estimation methods of an IIR filter with an autoregressive disturbance noise from the viewpoint of the observation data filtering. Firstly, we simply give the least mean square (LMS) algorithm for an IIR filter with autoregressive noise and derive a multi-innovation LMS (MI-LMS) algorithm for improving the parameter estimation accuracy. Secondly, we present a data filtering based LMS algorithm and a data filtering based MI-LMS algorithm for further improving the parameter estimation accuracy. The theoretical analyses show that the proposed algorithms are convergent and the simulation results indicate that the MI-LMS algorithm and the data filtering based MI-LMS algorithm are superior to the LMS algorithm and the data filtering based LMS algorithm in accuracy, respectively. The proposed methods in this paper have been extended to an IIR filter with autoregressive moving average noise. Finally, two simulation examples test the performances of the proposed algorithms.

Design of IIR Filter Using PSO Algorithm and its Implementation in FPGA

In this paper, Particle Swarm Optimization (PSO) algorithm based Adaptive Infinite Impulse Response (IIR) filter is designed. PSO algorithm is inspired by bird or fish swarm movement. PSO as global optimization technique provides advantages of simple implementation, quickly convergence providing better results and robustness for avoiding local minima problem. The designed PSO based Adaptive filter is then implemented to work as a noise canceller. Simulation results shows that the designed filter is effective with good noise suppression. The PSO based filter is also synthesized in FPGA and run fittings in small commercial devices Stratix-II and cyclone-II Keywords: Adaptive Infinite Impulse Response (IIR) Filter, FPGA implementation, Particle Swarm Optimization, VHDL