Adaptive infinite impulse response system identification using opposition based hybrid coral reefs optimization algorithm

An efficient global adaptive algorithm is required to determine the parameters of infinite impulse response (IIR) filter owing to the error cost surface of adaptive IIR system identification problem being generally nonlinear and non-differentiable. In this paper, a new bio-inspired algorithm, called opposition based hybrid coral reefs optimization algorithm (OHCRO) is applied for the IIR system identification problem. Coral reefs optimization algorithm (CRO) is a novel global algorithm, which mimics the behaviors of corals’ reproduction and coral reef formation. OHCRO is a modified version of CRO, on the one hand utilizing opposition based learning to accelerate global convergence, on the other hand cooperating with rotational direction method to enhance the local search capability. In addition, the Laplace broadcast spawning and power mutation brooding operator are used to maintain the diversity. The simulation studies have been performed for the performance comparison of genetic algorithm, particle swarm optimization and its variants, differential evolution and its variants and the proposed OHCRO for well-known benchmark examples with same order and reduced order filters. Simulation results and comparative studies justify the efficacy of the OHCRO based system identification approach in terms of convergence speed, identified coefficients and fitness values. In conclusion, OHCRO is a promising method for adaptive IIR system identification.     

High order stable infinite impulse response filter design using cuckoo search algorithm

In this paper, an efficient technique for optimal design of digital infinite impulse response (IIR) filter with minimum passband error (e _p ), minimum stopband error (e _s ), high stopband attenuation (A _s ), and also free from limit cycle effect is proposed using cuckoo search (CS) algorithm. In the proposed method, error function, which is multi-model and non-differentiable in the heuristic surface, is constructed as the mean squared difference between the designed and desired response in frequency domain, and is optimized using CS algorithm. Computational efficiency of the proposed technique for exploration in search space is examined, and during exploration, stability of filter is maintained by considering lattice representation of the denominator polynomials, which requires less computational complexity as well as it improves the exploration ability in search space for designing higher filter taps. A comparative study of the proposed method with other algorithms is made, and the obtained results show that 90% reduction in errors is achieved using the proposed method. However, computational complexity in term of CPU time is increased as compared to other existing algorithms.     

Mechatronic systems identification using an impulse response recursive algorithm

A recursive identification algorithm is used to identify mechatronic systems using impulse response data. The algorithm is based on an auto regressive moving average (ARMA) model with a steepest descent method to minimize the least square error between the original and predicted outputs. Two mechatronic systems are tested: DC motor and gyroscope. Impulse voltage input is used to excite the system and the angular speed output is measured. In both systems, the torque and angular velocity outputs are dependent on the voltage and current inputs. This relationship is governed by characteristics such as inductance, resistance, moment of inertia, friction, load, and system constants. Once the ARMA model is constructed, the transfer function is realized. Then the input voltage is varied and the identified model results are compared with the original system. Simulation results using Simulink and experimental results using Labview with data acquisition card (DAQ) are presented. Results show that the recursive identification algorithm is able to identify the two systems with minimal error.     

配送中心选址问题的和谐搜索算法

针对一类配送中心选址问题,建立了问题的数学模型,将和谐搜索算法进行改进并对问题进行求解,最后将此算法与最优保存算法(EGA)和遗传算法(GA)进行比较,验证了算法在计算结果方面的精确性和计算时间上的高效性。     

On optimal infinite impulse response edge detection filters

The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimal filter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated directly in the expression for spurious responses. The three criteria are maximized using the variational method and nonlinear constrained optimization. The optimal filter parameters are tabulated for various values of the filter performance criteria. A complete methodology for implementing the optimal filter using approximating recursive digital filtering is presented. The approximating recursive digital filter is separable into two linear filters, operating in two orthogonal directions. The implementation is very simple and computationally efficient. has a constant time of execution for different sizes of the operator, and is readily amenable to real-time hardware implementation     

Nonlinear system identification based on ANFIS-Hammerstein model using Gravitational search algorithm

The identification of real-world plants and processes, which are nonlinear in nature, represents a challenging problem. Currently, the Hammerstein model is one of the most popular nonlinear models. A Hammerstein model involves the combination of a nonlinear element and a linear dynamic system. On the other hand, the Adaptive-network-based fuzzy inference system (ANFIS) represents a powerful adaptive nonlinear network whose architecture can be divided into a nonlinear block and a linear system. In this paper, a nonlinear system identification method based on the Hammerstein model is introduced. In the proposed scheme, the system is modeled through the adaptation of an ANFIS scheme, taking advantage of the similarity between it and the Hammerstein model. To identify the parameters of the modeled system, the proposed approach uses a recent nature-inspired method called the Gravitational Search Algorithm (GSA). Compared to most existing optimization algorithms, GSA delivers a better performance in complex multimodal problems, avoiding critical flaws such as a premature convergence to sub-optimal solutions. To show the effectiveness of the proposed scheme, its modeling accuracy has been compared with other popular evolutionary computing algorithms through numerical simulations on different complex models.     

Harmony filter: A robust visual tracking system using the improved harmony search algorithm

In this article a novel approach to visual tracking called the harmony filter is presented. It is based on the Harmony Search algorithm, a derivative free meta-heuristic optimisation algorithm inspired by the way musicians improvise new harmonies. The harmony filter models the target as a colour histogram and searches for the best estimated target location using the Bhattacharyya coefficient as a fitness metric. Experimental results show that the harmony filter can robustly track an arbitrary target in challenging conditions. We compare the speed and accuracy of the harmony filter with other popular tracking algorithms including the particle filter and the unscented Kalman filter. Experimental results show the harmony filter to be faster and more accurate than both the particle filter and the unscented Kalman filter.     

Harmony search algorithm and combined index-based optimal reallocation of generators in a deregulated power system

Neural Computing and Applications - It is compulsory for the electrical industry to make effective utilization of the available resources and provide a stable and reliable supply to the consumers....     

Instantaneous impulse response of nonlinear system

Mathematical models for nonlinear systems, which admit both standard integrable and impulsive inputs are developed. It is shown that instantaneous response of a nonlinear system to an impulse is not, in general, single-valued, whereas a particular response can be selected through the construction of an auxiliary system. The complete integrability of the auxiliary system is proven to guarantee uniqueness of the impulse response     

Harmony search based algorithm for the optimum design of grillage systems to LRFD-AISC

Harmony search based optimum design method is presented for the grillage systems. This numerical technique imitates the musical performance process that takes place when a musician searches for a better state of harmony. Jazz improvisation seeks to find musically pleasing harmony similar to the optimum design process which seeks to find the optimum solution. The design algorithm considers the serviceability and ultimate strength constraints which are implemented from Load and Resistance Factor Design—American Institute of Steel Construction (LRFD-AISC). It selects the appropriate W-sections for the transverse and longitudinal beams of the grillage system out of 272 discrete W-section designations given in LRFD-AISC. This selection is carried out such that the design limitations described in LRFD-AISC are satisfied and the weight of the system is the minimum. Many design examples are considered to demonstrate the efficiency of the algorithm presented.     

A canonic-signed-digit coded genetic algorithm for designing finite impulse response digital filter

In this paper, the genetic algorithm (GA) based on Canonic Signed Digit (CSD) code was used to find the optimum design of a finite impulse response digital filter (FIR). By using the characteristics of the CSD structure, the circuit was able to be simplified and also the calculation speed was raised to increase the hardware's efficiency. However, CSD structure cannot be guaranteed by a general GA after the evolution of chromosomes. Thus in this research an algorithm was proposed which the CSD structure can be maintained. A CSD coded GA was used to the evolution of chromosome to reduce the time wasted by trials and errors during the evolution and then to accelerate the training speed. In this paper, a new hybrid code for the filter coefficients was proposed to improve the precision of the coefficient of FIR. An example is shown in this paper to verify the efficiency of the proposed algorithm.     

A linear search scheme for effective order of channel impulse response in OFDM systems

This paper deals with the estimation of the channel impulse response （CIR） in orthogonal frequency division multiplexing （OFDM） systems. In particular, we focus on the maximum likelihood （ML） estimator. The ML estimator is simpler to implement as it needs no information on the channel statistics, but its performance depends on the assumed effective order of channel impulse response （EOCIR）. In this paper, we propose a novel ML channel estimator which combines the ML estimation with a strategy of linear search. The proposed method introduces a few complexities, but, compared with the existing ML channel estimators, it is highly robust to variation of EOCIR Its performance has been proved using theoretical analysis and simulations.     

Fractional-order impulse response of the respiratory system

This paper presents the measurement, frequency-response modeling and identification, and the corresponding impulse time response of the human respiratory impedance and admittance. The investigated adult patient groups were healthy, diagnosed with chronic obstructive pulmonary disease and kyphoscoliosis, respectively. The investigated children patient groups were healthy, diagnosed with asthma and cystic fibrosis, respectively. Fractional order (FO) models are identified on the measured impedance to quantify the respiratory mechanical properties. Two methods are presented for obtaining and simulating the time-domain impulse response from FO models of the respiratory admittance: (i) the classical pole-zero interpolation proposed by Oustaloup in the early 90s, and (ii) the inverse discrete Fourier Transform (DFT). The results of the identified FO models for the respiratory admittance are presented by means of their average values for each group of patients. Consequently, the impulse time response calculated from the frequency response of the averaged FO models is given by means of the two methods mentioned above. Our results indicate that both methods provide similar impulse response data. However, we suggest that the inverse DFT is a more suitable alternative to the high order transfer functions obtained using the classical Oustaloup filter. Additionally, a power law model is fitted on the impulse response data, emphasizing the intrinsic fractal dynamics of the respiratory system.     

A new algorithm for linear system identification

This correspondence considers the on-line parameter identification of a forced linear discrete-time dynamic system from a sequence of white-noise-corrupted output measurements. In contrast to other approaches, the proposed stochastic approximation algorithm does not require knowledge of the noise statistics and converges to the true value of the parameters in the mean-square sense. If the input measurements are also corrupted with white noise, an additional term depending on the variance of the noise is required.     

An algorithm for nonlinear system parameter identification

An algorithm for system parameter identification will be presented in this paper. The method is applicable to linear and nonlinear systems with known structures. It is applied in this paper to systems in which both system and measurement noises can be neglected. The algorithm requires less time per iteration and less computer storage than the quasilinearization method. A shaft position control system with multiple nonlinearities will be used to illustrate the method.     

A hybrid job scheduling algorithm based on Tabu and Harmony search algorithms

The Journal of Supercomputing - Nowadays, cloud computing became a very important way of processing very large and complicated jobs and services. To serve millions of users with a high satisfaction...     

A nonparametric polynomial identification algorithm for theHammerstein system

Almost all existing Hammerstein system nonparametric identification algorithms can recover the unknown system nonlinear element up to an additive constant, and one functional value of the nonlinearity is usually assumed to be known to make the constant solvable. To overcome this defect, in this paper, a new nonparametric polynomial identification algorithm for the Hammerstein system is proposed which extends the idea in the author's previous work (1993, 1994) on the Hammerstein system identification to a more general and practical case, where no functional value of the system nonlinearity is known a priori. Convergence and convergence rates in both uniform and global senses are established, and simulation studies demonstrate the effectiveness and advantage of the new algorithm     

Harmony search algorithm for minimum cost design of steel frames with semi-rigid connections and column bases

Harmony search-based algorithm is developed to determine the minimum cost design of steel frames with semi-rigid connections and column bases under displacement, strength and size constraints. Harmony search (HS) is recently developed metaheuristic search algorithm which is based on the analogy between the performance process of natural music and searching for solutions of optimum design problems. The geometric non-linearity of the frame members, the semi-rigid behaviour of the beam-to-column connections and column bases are taken into account in the design algorithm. The results obtained by semi-rigid connection and column base modelling are also compared to one developed by rigid connection modelling. The efficiency of HS algorithm, in comparison with genetic algorithms (GAs), is verified with three benchmark examples. The results indicate that HS could obtain lighter frames and less cost values than those developed using GAs.     

An efficient hybrid genetic algorithm to design finite impulse response filters

Although genetic algorithms (GAs) have proved their ability to provide answers to the limitations of more conventional methods, they are comparatively inefficient in terms of the time needed to reach a repeatable solution of desired quality. An inappropriate selection of driving parameters is frequently blamed by practitioners. The use of hybrid schemes is interesting but often limited as they are computationally expensive and versatile. This paper presents a novel hybrid genetic algorithm (HGA) for the design of digital filters. HGA combines a pure genetic process and a dedicated local approach in an innovative and efficient way. The pure genetic process embeds several mechanisms that interact to make the GA self-adaptive in the management of the balance between diversity and elitism during the genetic life. The local approach concerns convergence of the algorithm and is highly optimized so as to be tractable. Only some promising reference chromosomes are submitted to the local procedure through a specific selection process. They are more likely to converge towards different local optima. This selective procedure is fully automatic and avoids excessive computational time costs as only a few chromosomes are concerned. The hybridization and the mechanisms involved afford the GA great flexibility. It therefore avoids laborious manual tuning and improves the usability of GAs for the specific area of FIR filter design. Experiments performed with various types of filters highlight the recurrent contribution of hybridization in improving performance. The experiments also reveal the advantages of our proposal compared to more conventional filter design approaches and some reference GAs in this field of application.     

Harmony K-means algorithm for document clustering

Fast and high quality document clustering is a crucial task in organizing information, search engine results, enhancing web crawling, and information retrieval or filtering. Recent studies have shown that the most commonly used partition-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm can generate a local optimal solution. In this paper we propose a novel Harmony K-means Algorithm (HKA) that deals with document clustering based on Harmony Search (HS) optimization method. It is proved by means of finite Markov chain theory that the HKA converges to the global optimum. To demonstrate the effectiveness and speed of HKA, we have applied HKA algorithms on some standard datasets. We also compare the HKA with other meta-heuristic and model-based document clustering approaches. Experimental results reveal that the HKA algorithm converges to the best known optimum faster than other methods and the quality of clusters are comparable.