Nonlinear system identification using genetic algorithm

Kumon, Toshiro; Iwasaki, Masaki; Suzuki, Tsuyoshi; Hashiyama, Tomonori; Matsui, Nobuyuki; Okuma, Shigeru

doi:10.1109/iecon.2000.972387

Cited by 19 publications

(10 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been reported in (Hakimi-M and Khaloozadeh, 2004) that the GA based approach proposed in (Kumon et al, 2000) can be applied only when the signal between the nonlinear and linear block of the system is available, which may not be feasible under all conditions. In (Hakimi-M and Khaloozadeh, 2004), a Hammerstein model identification scheme based on GA has been reported, in which the nonlinear block has been modelled using a neural network.…”

Section: Hammerstein Model Identificationmentioning

confidence: 98%

See 1 more Smart Citation

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

Gotmare

Bhattacharjee

Patidar

et al. 2017

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

Section: Hammerstein Model Identificationmentioning

confidence: 98%

“…The parameters of the linear block were obtained using a least squares approach. Parameter estimation of both non-linear and linear blocks of a Hammerstein model was attempted in (Kumon et al, 2000). However, the models obtained were sub-optimal.…”

Section: Hammerstein Model Identificationmentioning

confidence: 99%

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

Gotmare

Bhattacharjee

Patidar

et al. 2017

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

“…Resonant systems and vibration analysis are object of many study areas in engineering and widely applied in structural analysis, civil construction, aeronautics, electrical engineering and others. Several recent scientific research about resonant systems are addressed willing to obtain detailed description of its models, to pattern its resonant poles and behavior (Jeong et al, 2013;Noshadi et al, 2016;Kumon et al, 2000). Indeed, due to the oscillatory nature of resonant systems, it is highly recommended to investigate their operational limits via reliable simulations in order to avoid disruptions and injuries to equipment.…”

Section: Introductionmentioning

confidence: 99%

On the use of Frequency-Domain Subspace-based System Identification for Estimating Resonant Systems

Rodrigues

Oliveira

Santo³

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

View full text Add to dashboard Cite

In this paper, a parametric identification of high order resonant systems from frequency response data is analyzed. Among many techniques for obtaining black-box models for such systems, subspace-based algorithms stand out. Subspace methods uses geometric projections to obtain specific information necessary to compute the system extended observability matrix and, eventually, to obtain a state-space model representation. Throughout the paper, the method is also shown to use only well conditioned matrices to avoid numerical problems with high order models. The validity of the algorithm is firstly demonstrated on a simulated data of a synthetic 15th order system. Then, in order to state the systems effectiveness, the method is applied to actual data extracted from a resonant beam.

show abstract

“…Various heuristic approaches have been adopted by researchers for dual channel speech enhancement so far, for example Genetic algorithm [11]- [12], Particle Swarm Optimization and many of the variants of PSO.…”

Section: Introductionmentioning

confidence: 99%

A New Dual Channel Speech Enhancement Approach Based on Accelerated Particle Swarm Optimization (APSO)

Prajna¹,

Rao²,

Reddy³

et al. 2014

IJISA

View full text Add to dashboard Cite

─ This research paper proposes a recently developed new variant of Particle Swarm Optimization (PSO) called Accelerated Particle Swarm Optimization (APSO) in speech enhancement application. Accelerated Particle Swarm Optimization technique is developed by Xin she Yang in 2010. APSO is simpler to implement and it has faster convergence when compared to the standard PSO (SPSO) algorithm. Hence as an alternative to SPSO based speech enhancement algorithm, APSO is introduced to speech enhancement in the present paper. The present study aims to analyze the performance of APSO and to compare it with existing standard PSO algorithm, in the context of dual channel speech enhancement. Objective evaluation of the proposed method is carried out by using three objective measures of speech quality SNR, Improved SNR, PESQ and one objective measure of speech intelligibility FAI. The performance of the algorithm is studied under babble and factory noise environments. Simulation result proves that APSO based speech enhancement algorithm is superior to the standard PSO based algorithm with an improved speech quality and intelligibility measures.

show abstract

Nonlinear system identification using genetic algorithm

Cited by 19 publications

References 3 publications

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

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

On the use of Frequency-Domain Subspace-based System Identification for Estimating Resonant Systems

A New Dual Channel Speech Enhancement Approach Based on Accelerated Particle Swarm Optimization (APSO)

Contact Info

Product

Resources

About