Classification of chaos-based digital modulation techniques using wavelet neural networks and performance comparison of wavelet families

Türk, Mustafa; Oğraş, Hidayet

doi:10.1016/j.eswa.2010.08.045

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…Here a fu zzy inference system co mprises of the fuzzy model [19,20] proposed by Takagi, Sugeno and Kang to formalize a systematic approach to generate fuzzy rules fro m an input output data set. More details regarding ANFIS can be found in [21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: A Adaptive Network Based Fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…For simp licity, it is assumed that the fuzzy inference system under consideration has two inputs and one output. The rule base contains two fu zzy if-then ru les of Takagi and Sugeno'stype [21] as follows:…”

Section: A Adaptive Network Based Fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…In this paper, for obtaining the compact set of features that capture the prominent characteristics of the CCPs in a relatively small nu mber of the co mponents, the multi-resolution wavelets analysis (MRWA) is proposed. Nowadays, in many areas such as image processing, signal processing, especially image co mpression, speech processing, and computer vision, the wavelet transform types are commonly used [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recognition of Control Chart Patterns Using Imperialist Competitive Algorithm and Fuzzy Rules Approach

Mirzaei¹,

Nikpey²,

Zarbakhsh³

2014

IJISA

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Traditionally, Control Chart Patterns (CCP) is widely used as a powerful method to measure, classify,analyze and interpret process data to improve the quality of products and service by detecting instabilities and justifying possible causes. In this study, we have developed an expert system that we called an expert system for control chart patterns recognition for recognition of the common types of control chart patterns (CCPs). The proposed system includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, the multi-resolution wavelets (M RW) are proposed as the effective features for representation of CCPs. In the classifier module, the adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has a very important role for its recognition accuracy. Therefore, in the optimization module, imperialist competitive algorithm(ICA) is proposed for finding optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.

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Section: A Adaptive Network Based Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Section: A Adaptive Network Based Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recognition of Control Chart Patterns Using Imperialist Competitive Algorithm and Fuzzy Rules Approach

Mirzaei¹,

Nikpey²,

Zarbakhsh³

2014

IJISA

View full text Add to dashboard Cite

show abstract

“…In this paper, for obtaining the compact set of features that capture the prominent characteristics of the CCPs in a relatively small number of the components, the multi-resolution wavelets analysis (MRWA) is proposed. Nowadays, in many areas such as image processing, signal processing, especially image compression, speech processing, and computer vision, the wavelet transform types are commonly used [20][21][22]. More details regarding the MRWA can be found in Ref.…”

Section: Introductionmentioning

confidence: 99%

Control chart patterns recognition using optimized adaptive neuro-fuzzy inference system and wavelet analysis

Bayat¹,

Gharekhani²,

Mohajeran³

et al. 2013

J Eng Technol

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“…Wavelet transform-based features were also utilized for the discrimination of digital modulation types in the literature [40], [41]. One drawback of these proposed recognition methods is that, with only AWGN channel, they were unable to recognize advanced modulation schemes such as QAM signals.…”

Section: Introductionmentioning

confidence: 99%

Dual-Determination of Modulation Types and Signal-to-Noise Ratios Using 2D-ASIQH Features for Next Generation of Wireless Communication Systems

et al. 2021

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In order to pursue rapid development of the new generation of wireless communication systems and elevate their security and efficiency, this paper proposes a novel scheme for automatic dual determination of modulation types and signal to noise ratios (SNR) for next generations of wireless communication systems, fifth-generation (5G) and beyond. The proposed scheme adopts unique signatures depicted in two-dimensional asynchronously sampled in-phase-quadrature amplitudes' histograms (2D-ASIQHs)-based images and applies the support vector machines (SVMs) tool. Along with the estimation of the instantaneous SNR values over 0-35 dB range, the determination of nine modulation types that belong to different modulation categories i.e., phase-shift keying (Binary-PSK, Quadrature-PSK, and 8-PSK), amplitude-shift keying (2-ASK and 4-ASK) and quadrature-amplitude modulation (4-QAM, 16-QAM, 32-QAM, and 64-QAM) could be achieved by this scheme. The application of this scheme has been simulated using a channel model that is impaired by additive white Gaussian noise (AWGN) and Rayleigh fading, covering a broad range of SNRs of 0-35 dB. The performance of this dual-determination scheme shows high modulation recognition accuracy and low mean SNR estimation error. Therefore, it can be a better alternative for designers of next generation wireless communication systems.

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Classification of chaos-based digital modulation techniques using wavelet neural networks and performance comparison of wavelet families

Cited by 8 publications

References 12 publications

Recognition of Control Chart Patterns Using Imperialist Competitive Algorithm and Fuzzy Rules Approach

Recognition of Control Chart Patterns Using Imperialist Competitive Algorithm and Fuzzy Rules Approach

Control chart patterns recognition using optimized adaptive neuro-fuzzy inference system and wavelet analysis

Dual-Determination of Modulation Types and Signal-to-Noise Ratios Using 2D-ASIQH Features for Next Generation of Wireless Communication Systems

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