A novel evolutionary method of structure-diversified digital filter design and its experimental study
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Digital filters are now key components in many modern digital systems. This paper proposed a novel evolutionary method to design structure-diversified digital filters. On the investigation of the existing evolution-based digital filter design methods, most state-of-the-art works concentrate on the evolution of appropriate transfer functions for digital filters. However, a transfer function is not equivalent to a practical digital filter that has proper structure and can be implemented by a hardware directly. Some researchers proposed a synthesis method to generate the structure of digital filter from a specified transfer function. However, this method needs an existing transfer function as the prior knowledge. Compared with existing works on the evolution of digital filters, the proposed method is novel at the following aspect: the proposed method can directly evolve the structure and parameters of digital filter without the pre-definition of the transfer function. The only prior knowledge we need is the specification of the design target, such as the frequency range of the passband and stop-band. In the experimental study, a significant characteristic is revealed that the proposed method is able to evolve structure-diversified filters with approximate frequency response. The proposed method is a prototype, and it is demonstrated to be a promising way of digital filter design.
KeywordsEvolution Digital filter Structure diversity Digital filter encoding
This study was funded by the Foundation of Education Department, Henan Province, China (Grant Nos. 15A510018, 15A510019). This study was also funded by the Foundation of Technology Department, Henan Province, China (Grant No. 142102210629).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Chen H (2002) The matrix expression of signal flow graph and its application in system analysis software. Chin J Electron 11:361–363Google Scholar
- Lohn JD, Colombano SP (1999) A circuit representation technique for automated circuit design. IEEE Trans Evolut Comput 3(3):205–219. doi: 10.1109/4235.788491
- Proakis J, Manolakis D (2007) Digital signal processing: principles, algorithms, and applications, 4th edn. Prentice Hall, New JerseyGoogle Scholar
- Sayilir S, Loke W-F, Lee J, Diamond H, Epstein B, Rhodes DL, Jung B (2014) A—90 dBm sensitivity wireless transceiver using VCO-PA-LNA-switch-modulator co-design for low power insect-basedwireless sensor networks. IEEE J Solid-State Circuits 49:996–1006. doi: 10.1109/JSSC.2013.2293022 CrossRefGoogle Scholar
- Shao P, Wu Z, Zhou X, Tran DC (2015) FIR digital filter design using improved particle swarm optimization based on refraction principle. Soft Comput. doi: 10.1007/s00500-015-1963-3
- Singh R, Verma HK (2013) Teaching learning-based optimization algorithm for parameter identification in the design of IIR filters. J Inst Eng (India): Ser B 94:285–294. doi: 10.1007/s40031-013-0063-y
- Vasundhara, Mandal D, Kar R, Ghoshal SP (2014) Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization. Nat Comput 13:55–64. doi: 10.1007/s11047-013-9381-x