Adaptive Infinite Impulse Response Filters Research Articles

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.

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.

Real-Time MPPT Optimization of PV Systems by Means of DCD-RLS Based Identification

Maximum power point tracking (MPPT) algorithms continuously change duty cycle of a power converter to extract maximum power from photovoltaic panels. In all of MPPT methods, two parameters, i.e., perturbation period $(T_{p})$ and amplitude ${(\Delta \text{D)}}$ , have a great effect on speed and accuracy of MPPT. Optimum value of the perturbation period is equal to the system settling time, which is the system model-dependent parameter. Since the system model varies according to the change of irradiance level and temperature, the value of $T_{p}$ has to be determined online. In this paper, the parametric identification method is adopted to identify the online value of $T_{p}$ . The proposed method is based on the dichotomous coordinate descent-recursive least squares algorithm and uses an infinite impulse response adaptive filter as the system model. Computation of this algorithm is based on an efficient, fixed-point, and iterative approach with no explicit division operations; these features are highly suitable for online applications. As a result, the proposed method compared to previous works leads to more accurate and faster identification of the system settling time. To test and validate the proposed method, it has been simulated and implemented to be further validated with experimental data.

Comprehensive Optimal Fir Filter Design Procedures With Various Impacts

Digital-signal-processing (DSP) is one of the recent emerging techniques contain more filtering operations. It may an image type or audio/ video signal processing. Each processing unit has filtering sections to filter noise elements. Hence, there is a need for efficient and secure algorithmic scheme. Here, a exhaustive scrutiny use of complex optimization algorithms towards the digital-filter construction is conferred. In appropriate, the scrutiny target on the identification of various suggestions and limitations in FIR system design. For exact representations, the infinite impulse response adaptive filters and finite impulse response models are considered for estimation. It is designed to review a various swarm and evolutionary computing structures employed for filter design schemes. Some popular computing algorithms are noticed to recover characteristics of percolate design approach. Further, compared with recent research for identifying the updating features in optimization schemes. Finally, this review suggested that the swarm intelligence based researchers improved the constraints and its attributes.

Multivariate Shannon's entropy for adaptive IIR filtering via kernel density estimators

In supervised infinite impulse response adaptive filtering, approximate gradient-based approaches are the usual option among optimisation methods. When based on the mean squared error (MSE) criterion, however, these approaches may present biased solutions in noisy scenarios. In that sense, instead of the MSE, the authors propose the use of Shannon's error entropy, an information theoretic learning criterion, which is able to extract higher order statistics from the underlying signals. In particular, a multivariate entropy definition is considered, which is applied to derive a Recursive Prediction Error-based algorithm. The performance analyses are carried out in the context of the supervised channel equalisation problem, with results very favourable to the proposal, in high and low noise level environments.

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.

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.

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.

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Abstract An exhaustive review on the use of structured stochastic search approaches towards system identification and digital filter design is presented in this paper. In particular, the paper focuses on the identification of various systems using infinite impulse response adaptive filters and Hammerstein models as well as on the estimation of chaotic systems. In addition to presenting a comprehensive review on the various swarm and evolutionary computing schemes employed for system identification as well as digital filter design, the paper is also envisioned to act as a quick reference for a few popular evolutionary computing algorithms.

A new design method based on firefly algorithm for IIR system identification problem

In this paper a population based evolutionary optimization methodology called firefly algorithm (FFA) is applied for the optimization of system coefficients of the infinite impulse response (IIR) system identification problem. FFA is inspired by the flash pattern and characteristics of fireflies. In FFA technique, behaviour of flashing firefly towards its competent mate is structured. In this algorithm attractiveness depends on brightness of light and a bright firefly feels more attraction for the brighter one. For this optimization problem, brightness varies inversely proportional to the error fitness value, so the position of the brightest firefly gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic movement equation results in balancing of exploration and exploitation of search space. The proposed FFA based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using FFA over GA, PSO and DE in terms of convergence speed, identifying plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.

IIR system identification using differential evolution with wavelet mutation

In this paper an improved version of Differential Evolution (DE) technique called Differential Evolution with Wavelet Mutation (DEWM) is applied to the infinite impulse response (IIR) system identification problem. Instead of fixed value of scaling factor in standard DE, an iteration dependent scaling factor governed by the wavelet function during the mutation process is adopted in the proposed technique. This modification in the mutation process ensures not only the faster searching in the multidimensional search space but also the solution produced is very close to the global optimal solution. Apart from this, the proposed technique DEWM has alleviated from inherent drawbacks of premature convergence and stagnation, unlike Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using DEWM over GA, PSO and DE in terms of convergence speed, plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.

A novel design method for optimal IIR system identification using opposition based harmony search algorithm

In this paper a population based evolutionary optimization methodology called Opposition based Harmony Search Algorithm (OHS) is applied for the optimization of system coefficients of adaptive infinite impulse response (IIR) system identification problem. The original Harmony Search (HS) algorithm is chosen as the parent one and opposition based approach is applied to it with an intention to exhibit accelerated near global convergence profile. During the initialization, for choosing the randomly generated population/solution opposite solutions are also considered and the fitter one is selected as apriori guess for having faster convergence profile. Each solution in Harmony Memory (HM) is generated on the basis of memory consideration rule, a pitch adjustment rule and a re-initialization process which gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic HS algorithm results in balancing of exploration and exploitation of search space. The proposed OHS based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed OHS based system identification approach over GA, PSO and DE in terms of convergence speed, identifying the system plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.

A novel social emotional optimisation algorithm for IIR system identification problem

In this paper an evolutionary optimisation methodology based on social emotional optimisation algorithm (SEOA) is applied to the infinite impulse response (IIR) system identification problem. In SEOA methodology, behaviour of human beings for achieving higher social status in society is structured. In this virtual world, the individual with the highest rank in society gives the optimal solution in multidimensional search space. Earning the highest social status by means of cooperation and competition with others not only results in better exploration and exploitation of problem space but also ensures faster convergence to optimal solution. The proposed SEOA based system identification approach has resolved the inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimisation (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using SEOA over GA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.

Craziness based particle swarm optimization algorithm for IIR system identification problem

In this paper a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) technique is applied to the infinite impulse response (IIR) system identification problem. A modified version of PSO, called CRPSO adopts a number of random variables for having better and faster exploration and exploitation in multidimensional search space. Incorporation of craziness factor in the basic velocity expression of PSO not only brings diversity in particles but also ensures convergence to optimal solution. The proposed CRPSO based system identification approach has alleviated from the inherent drawbacks of premature convergence and stagnation, unlike real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using CRPSO over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.

A hybrid gradient‐based and differential evolution algorithm for infinite impulse response adaptive filtering

SUMMARYGlobal optimization algorithms (GO) had been applied to solve the adaptive infinite impulse response filtering problem, which is known to have multimodal error surface under certain conditions. However, although GO may be able to search multimodal surfaces, they have certain disadvantages. They may not converge to any minimum point, the convergence speed is reduced as the solution vectors move closer, and tracking ability for non‐stationary environment is lacking. The traditional gradient descent method does not have these limitation but is not able to search multimodal surfaces. In this work, we propose a hybrid algorithm combining gradient descent and differential evolution (DE) for adapting the coefficients of infinite impulse response adaptive filters. DE is run in a block‐based manner. The coefficient vector with the lowest error surface value (the best member) of the current block is updated via gradient descent for the duration of the next block. Thus combining the ability to search multimodal surface of DE and fast local search of gradient descent. As with all GO, global search capacity is gradually lost as the coefficient vectors converge together. Thus, re‐initialization is also incorporated into the hybrid algorithm to provide continuous global search capacity for non‐stationary environment. All the coefficient vectors except the best member are re‐initialized when the normalized mean Euclidean distance between each pair of vectors falls below a threshold value. Simulation results show that the proposed algorithm achieves better solution quality and convergence speed than classic DE and GO for stationary and non‐stationary environments. Copyright © 2013 John Wiley & Sons, Ltd.

Analysis and implementation of a structural vibration control algorithm based on an IIR adaptive filter

With the wide application of large-scale flexible structures in spacecraft, vibration control problems in these structures have become important design issues. The filtered-X least mean square (FXLMS) algorithm is the most popular one in current active vibration control using adaptive filtering. It assumes that the source of interference can be measured and the interference source is considered as the reference signal input to the controller. However, in the actual control system, this assumption is not accurate, because it does not consider the impact of the reference signal on the output feedback signal. In this paper, an adaptive vibration active control algorithm based on an infinite impulse response (IIR) filter structure (FULMS, filtered-U least mean square) is proposed. The algorithm is based on an FXLMS algorithm framework, which replaces the finite impulse response (FIR) filter with an IIR filter. This paper focuses on the structural design of the controller, the process of the FULMS filtering control method, the design of the experimental model object, and the experimental platform construction for the entire control system. The comparison of the FXLMS algorithm with FULMS is theoretically analyzed and experimentally validated. The results show that the FULMS algorithm converges faster and controls better. The design of the FULMS controller is feasible and effective and has greater value in practical applications of aerospace engineering.

An Adaptive Digital Control Technique for Improved Performance of Grid Connected Inverters

Grid connected voltage source inverters may be controlled in the stationary reference frame by means of the infinite impulse response (IIR) P+Resonat regulator. This regulator is able to correctly track fixed frequency sinusoidal references, but does not perform well if the frequency of the electric grid voltage is varied. In order to avoid the lack of precision to track variable frequency sinusoidal references, an adaptive IIR filter structure is proposed which offers good tracking properties even if the frequency of the grid voltage varies. This filter adapts its coefficients in real time and is inherently stable no matter the adaptation process, thus overcoming one of the most important drawbacks of the IIR filter structure. Furthermore, this structure is perfectly suited to be programmed in fixed point digital signal processors (DSPs) because of some important numeric properties, i.e., it has a high mapping precision and a low round-off accumulation, and it avoids quantization limit cycle oscillations. The proposed adaptive controller has been tested by means of the TI TMS320F2812 DSP. The obtained experimental results show up that this controller allows the correct tracking of a sinusoidal reference, even if this reference is time variant.

Active Online System Identification of Switch Mode DC–DC Power Converter Based on Efficient Recursive DCD-IIR Adaptive Filter

This paper introduces a novel technique for online system identification. Specific attention is given to the parameter estimation of dc-dc switched-mode power converters; however, the proposed method can be applied to many alternative applications where efficient and accurate parameter estimation is required. The proposed technique is computationally efficient, based on a dichotomous coordinate descent algorithm, and uses an infinite impulse response adaptive filter as the plant model. The system identification technique reduces the computational complexity of existing recursive least squares algorithms. Importantly, the proposed method is also able to identify the parameters quickly and accurately, thus offering an efficient hardware solution that is well suited to real-time applications. Simulation analysis and validation based on experimental data obtained from a prototype synchronous dc-dc buck converter is presented. Results clearly demonstrate that the estimated parameters of the dc-dc converter are a very close match to those of the experimental system. The approach can be directly embedded into adaptive and self-tuning digital controllers to improve the control performance of a wide range of industrial and commercial applications.

Modified PSO based Adaptive IIR Filter Design for System Identification on FPGA

programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. This paper focuses on the implementation of Adaptive Infinite Impulse response (IIR) filter on an FPGA using Modified Particle Swarm Optimization (PSO) Algorithm. The proposed Filter is capable of finding the global optimum solution for system identification problem in less number of iterations. The modified PSO algorithm has been developed and simulated using MATLAB. The result shows the enhanced speed of purposed design in terms of number of iterations it takes to identify the unknown system. The same algorithm has also been realized on various Xilinx FPGA devices and performances have also been analyzed. The area utilization by the proposed design on different FPGA devices has been compared. The results show that proposed filter is consuming very less area in terms of LUTs and Slices to provide enhanced area efficiency.