Application of evolutionary algorithm to design minimal phase digital filters with non-standard amplitude characteristics and finite bit word length

Słowik, Adam

doi:10.2478/v10175-011-0016-z

Cited by 17 publications

(8 citation statements)

References 13 publications

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“…For practical application the method should be relatively easy to configure and it should not be too sensitive to configuration parameters. The complexity of the method is not so important, as modern data processing units becomes more advanced and reliable [19], allowing signal processing tools to be more complex and computationally demanding [20]. Table 1 presents a comparison of the discussed methods.…”

Section: Configuration Of Methodsmentioning

confidence: 99%

Comparison of Advanced Signal-Processing Methods for Roller Bearing Faults Detection

Urbanek¹,

Barszcz²,

Uhl³

2012

Metrology and Measurement Systems

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Wind turbines are nowadays one of the most promising energy sources. Every year, the amount of energy produced from the wind grows steadily. Investors demand turbine manufacturers to produce bigger, more efficient and robust units. These requirements resulted in fast development of condition-monitoring methods. However, significant sizes and varying operational conditions can make diagnostics of the wind turbines very challenging. The paper shows the case study of a wind turbine that had suffered a serious rolling element bearing (REB) fault. The authors compare several methods for early detection of symptoms of the failure. The paper compares standard methods based on spectral analysis and a number of novel methods based on narrowband envelope analysis, kurtosis and cyclostationarity approach. The very important problem of proper configuration of the methods is addressed as well. It is well known that every method requires setting of several parameters. In the industrial practice, configuration should be as standard and simple as possible. The paper discusses configuration parameters of investigated methods and their sensitivity to configuration uncertainties

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Section: Configuration Of Methodsmentioning

confidence: 99%

Comparison of Advanced Signal-Processing Methods for Roller Bearing Faults Detection

Urbanek¹,

Barszcz²,

Uhl³

2012

Metrology and Measurement Systems

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“…Numerous examples include mechanical design [17] and signal processing [18] tasks. In particular, PSO is a stochastic optimization algorithm.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Particle swarm optimization of an iterative learning controller for the single-phase inverter with sinusoidal output voltage waveform

Ufnalski

Grzesiak

Gałkowski

2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. This paper presents the application of a particle swarm optimization (PSO) to determine iterative learning control (ILC) law gains for an inverter with an LC output filter. Available analytical tuning methods derived for a given type of ILC law are not very straightforward if additional performance requirements of the closed-loop system have to be met. These requirements usually concern the dynamics of a response to a reference signal, the dynamics of a disturbance rejection, the immunity against expected level of system and measurement noise, the robustness to anticipated variations of parameters, etc. An evolutionary optimization approach based on the swarm intelligence is proposed here. It is shown that in the case of the ILC applied to the LC filter, a cost function based on mean squares can produce satisfactory tuning effects. The efficacy of the procedure is illustrated by performing the optimization for various noise levels and various requested dynamics.

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“…QIGA algorithms use quantum mechanics concepts including qubits and superposition of states. QIGA algorithms have been successfully applied to a broad range of search and optimization problems [5,6,7]. The algorithms have demonstrated their particular efficacy for solving complex optimization problems.…”

Section: Introdutionmentioning

confidence: 99%

Higher-Order Quantum-Inspired Genetic Algorithms

Nowotniak

Kucharski

2014

Annals of Computer Science and Information Systems

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Abstract-This paper presents a theory and an empirical evaluation of Higher-Order Quantum-Inspired Genetic Algorithms. Fundamental notions of the theory have been introduced, and a novel Order-2 Quantum-Inspired Genetic Algorithm (QIGA2) has been developed. Contrary to all QIGA algorithms which represent quantum genes as independent qubits, in higherorder QIGAs quantum registers are used to represent genes strings, which allows modelling of genes relations using quantum phenomena. Performance comparison has been conducted on a benchmark of 20 deceptive combinatorial optimization problems. It has been presented that using higher quantum orders is beneficial for genetic algorithm efficiency, and the new QIGA2 algorithm outperforms the old QIGA algorithm tuned in highly compute-intensive metaoptimization process. I. INTRODUTIONR ESEARCH on quantum-inspired computational intelligence techniques was started by Narayann [1] in 1996, and the first proposal of Quantum-Inspired Genetic Algorithm (QIGA1) has been presented by Han and Kim in [2]. QuantumInspired Genetic Algorithms belong to a new class of artificial intelligence techniques, drawing inspiration from both evolutionary [3] and quantum [4] computing. Current literature on the subject consists of about a few hundreds scientific papers. Only a few papers attempt to theoretically analyse the properties of that class of algorithms. Among those there are i.a. [22,28], which has been emphasized in conclusions of recent comprehensive surveys [18,29].In QIGA algorithms, representation and genetic operators are based on computationally useful aspects of both biological evolution and unitary evolution of quantum systems. QIGA algorithms use quantum mechanics concepts including qubits and superposition of states. QIGA algorithms have been successfully applied to a broad range of search and optimization problems [5,6,7]. The algorithms have demonstrated their particular efficacy for solving complex optimization problems. Recent years have witnessed successful applications of Quantum-Inspired Genetic Algorithms in a variety of fields, including image processing [8,9,10], flow shop scheduling [11,12], thermal unit commitment [13,14], power system optimization [15,16], localization of mobile robots [17] and many others.For a current and comprehensive survey of QuantumInspired Genetic Algorithms and the necessary background of Quantum Computing and Quantum-Inspired Computational Intelligence techniques, the reader is referred to [1,2,18,29].This work was supported in part by PL-Grid Infrastructure This paper is structured as follows. In Section 1, an introductory background and the most important references for the subject field have been given. In Section 2, the theory of Higher-Order Quantum-Inspired Genetic Algorithms has been presented. In Section 3, details of the original Order-2 Quantum-Inspired Genetic Algorithm have been provided. In Section 4, experimental results have been provided and evaluated. In Section 5, the article has been briefly summarized, final conclusi...

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Application of evolutionary algorithm to design minimal phase digital filters with non-standard amplitude characteristics and finite bit word length

Cited by 17 publications

References 13 publications

Comparison of Advanced Signal-Processing Methods for Roller Bearing Faults Detection

Comparison of Advanced Signal-Processing Methods for Roller Bearing Faults Detection

Particle swarm optimization of an iterative learning controller for the single-phase inverter with sinusoidal output voltage waveform

Higher-Order Quantum-Inspired Genetic Algorithms

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