Optimization of quantum-inspired neural network using memetic algorithm for function approximation and chaotic time series prediction
•A novel memetic algorithm based on hybrid genetic algorithm and gradient descent is proposed.•We develop a new and efficient type of quantum-inspired neural networks model.•The accuracy of the approach is investigated for function approximation and time series prediction problems.•Numerical experim...
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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 291; s. 175 - 186 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
24.05.2018
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| Témata: | |
| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | •A novel memetic algorithm based on hybrid genetic algorithm and gradient descent is proposed.•We develop a new and efficient type of quantum-inspired neural networks model.•The accuracy of the approach is investigated for function approximation and time series prediction problems.•Numerical experiments show the excellent effectiveness and efficiency of the proposed approach.
Heuristic and deterministic optimization methods are extensively applied for the training of artificial neural networks. Both of these methods have their own advantages and disadvantages. Heuristic stochastic optimization methods like genetic algorithm perform global search, but they suffer from the problem of slow convergence rate near global optimum. On the other hand deterministic methods like gradient descent exhibit a fast convergence rate around global optimum but may get stuck in a local optimum. Motivated by these problems, a hybrid learning algorithm combining genetic algorithm (GA) with gradient descent (GD), called HGAGD, is proposed in this paper. The new algorithm combines the global exploration ability of GA with the accurate local exploitation ability of GD to achieve a faster convergence and also a better accuracy of final solution. The HGAGD is then employed as a new training method to optimize the parameters of a quantum-inspired neural network (QINN) for two different applications. Firstly, two benchmark functions are chosen to demonstrate the potential of the proposed QINN with the HGAGD algorithm in dealing with function approximation problems. Next, the performance of the proposed method in forecasting Mackey–Glass time series and Lorenz attractor is studied. The results of these studies show the superiority of the introduced approach over other published approaches. |
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| AbstractList | •A novel memetic algorithm based on hybrid genetic algorithm and gradient descent is proposed.•We develop a new and efficient type of quantum-inspired neural networks model.•The accuracy of the approach is investigated for function approximation and time series prediction problems.•Numerical experiments show the excellent effectiveness and efficiency of the proposed approach.
Heuristic and deterministic optimization methods are extensively applied for the training of artificial neural networks. Both of these methods have their own advantages and disadvantages. Heuristic stochastic optimization methods like genetic algorithm perform global search, but they suffer from the problem of slow convergence rate near global optimum. On the other hand deterministic methods like gradient descent exhibit a fast convergence rate around global optimum but may get stuck in a local optimum. Motivated by these problems, a hybrid learning algorithm combining genetic algorithm (GA) with gradient descent (GD), called HGAGD, is proposed in this paper. The new algorithm combines the global exploration ability of GA with the accurate local exploitation ability of GD to achieve a faster convergence and also a better accuracy of final solution. The HGAGD is then employed as a new training method to optimize the parameters of a quantum-inspired neural network (QINN) for two different applications. Firstly, two benchmark functions are chosen to demonstrate the potential of the proposed QINN with the HGAGD algorithm in dealing with function approximation problems. Next, the performance of the proposed method in forecasting Mackey–Glass time series and Lorenz attractor is studied. The results of these studies show the superiority of the introduced approach over other published approaches. |
| Author | Tofighi, Morteza Ganjefar, Soheil |
| Author_xml | – sequence: 1 givenname: Soheil orcidid: 0000-0001-7030-2766 surname: Ganjefar fullname: Ganjefar, Soheil email: s_ganjefar@basu.ac.ir – sequence: 2 givenname: Morteza surname: Tofighi fullname: Tofighi, Morteza |
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| Cites_doi | 10.1109/91.649903 10.1016/j.bspc.2015.10.008 10.1016/j.amc.2012.04.069 10.1016/j.apm.2006.04.014 10.1016/j.knosys.2017.09.013 10.1016/j.ins.2016.07.062 10.1016/j.chaos.2017.03.018 10.1023/A:1019708909383 10.1016/j.neucom.2012.12.073 10.1016/j.eswa.2015.04.018 10.1049/el:19960229 10.1109/TMTT.2005.862671 10.1016/j.neunet.2015.07.013 10.1016/j.cie.2014.12.013 10.1016/j.neucom.2017.04.025 10.1109/TNNLS.2012.2188414 10.1016/j.fss.2010.06.002 10.1016/j.neucom.2012.01.014 10.1109/TSP.2004.837418 10.1109/TEVC.2005.850260 10.1016/j.neunet.2016.01.002 10.1016/j.neucom.2013.01.029 10.1080/08839514.2011.529263 10.1016/j.physa.2014.07.071 10.1016/j.ins.2014.09.054 10.1016/j.cnsns.2010.12.011 10.1109/91.917126 10.1016/j.ins.2014.08.033 10.1016/j.ins.2004.10.005 10.1016/j.neucom.2007.07.018 10.1007/BF01342185 10.1109/TNNLS.2015.2404823 10.1016/j.eswa.2011.09.040 10.1016/j.neucom.2017.01.032 10.1016/j.eswa.2014.11.056 10.1016/j.asoc.2015.05.034 10.1016/j.asoc.2014.09.007 10.1016/j.neunet.2013.02.012 10.1016/j.asoc.2015.08.009 10.1016/j.sna.2008.05.025 10.1109/72.165591 10.1109/21.199466 10.1016/S0925-2312(01)00338-1 10.1016/j.procbio.2015.12.005 10.1016/j.neucom.2009.11.007 10.1016/0165-0114(95)00322-3 |
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| Keywords | Memetic algorithm Time series prediction Quantum-inspired neural network Function approximation Genetic algorithm |
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| References | Nielsen, Chuang (bib0026) 2010 Zhao, Xu, Jiang (bib0010) 2015; 42 Almeida, Ludermir (bib0012) 2010; 73 Shadmand, Mashoufi (bib0004) 2016; 25 Rojas, Valenzuela, Rojas, Guillen, Herrera, Pomares, Marquez, Pasadas (bib0042) 2008; 71 Benioff (bib0018) 1982; 29 Chen, Yang, Dong, Abraham (bib0040) 2005; 174 Tripathy, Dash, Padhy (bib0003) 2015; 80 Cui, Shi, Wang (bib0023) 2015; 71 Wang, Mendel (bib0039) 1992; 22 Mirjalili, Hashim, Sardroudi (bib0013) 2012; 218 Takahashi, Rocha, Núñez (bib0001) 2016; 51 Zhao, Huang (bib0015) 2007; 31 Kobayashi (bib0005) 2017; 260 Kouda, Matsui, Nishimura (bib0021) 2002; 16 Chandra, Zhang (bib0045) 2012; 86 Melin, Soto, Castillo, Soria (bib0043) 2012; 39 Li, Xiao, Shang, Tong, Li, Cao (bib0027) 2013; 117 Requena-Pérez, Albero-Ortiz, Monzó-Cabrera, Díaz-Morcillo (bib0014) 2006; 54 Martínez-Martínez, Gomez-Gil, Gomez-Gil, Ruiz-Gonzalez (bib0002) 2015; 42 da Silva, de Oliveira (bib0019) 2016; 370–371 Cho, Wang (bib0041) 1996; 83 Cao, Cao, Wang (bib0022) 2015; 290 Chandra (bib0047) 2015; 26 Sun, Du, Gong, Ma, Wang (bib0007) 2014; 415 Liu, Chen, Chang, Shih (bib0029) 2013; 45 Zhongda, Shujiang, Yanhong, Yi (bib0049) 2017; 98 Zhang, Benveniste (bib0031) 1992; 3 Mousavi, Alfi (bib0009) 2015; 36 Mu, Xie, Liu, Chen, Liu, Jiao (bib0008) 2015; 34 Hu, Bao, Xiong (bib0011) 2014; 25 Jang, Sun, Mizutani (bib0044) 1997 Tzeng (bib0033) 2010; 161 Dhahri, Alimi (bib0048) 2006 Ardalani-Farsa, Zolfaghari (bib0046) 2011; 25 Krasnogor, Smith (bib0006) 2005; 9 Han, Xi, Xu, Yin (bib0016) 2004; 52 Yao, Wei, He (bib0032) 1996; 32 da Silva, Ludermir, de Oliveira (bib0020) 2016; 76 Ho, Zhang, Xu (bib0036) 2001; 9 Rojas, Pomares, Bernier, Ortega, Pino, Pelayo, Prieto (bib0037) 2002; 42 Gao, Ma, Song, Liu (bib0030) 2017; 238 Takahashi, Kurokawa, Hashimoto (bib0025) 2014; 134 Kim, Kim (bib0038) 1997; 5 Li, Han, Wang (bib0017) 2012; 23 Ebadat, Noroozi, Safavi, Mousavi (bib0034) 2011; 16 Ma, Niu, Zhang, Li (bib0028) 2017; 136 Shen, Huang, Hwang (bib0024) 2008; 147 Ganjefar, Tofighi (bib0035) 2015; 294 Benioff (10.1016/j.neucom.2018.02.074_bib0018) 1982; 29 Han (10.1016/j.neucom.2018.02.074_bib0016) 2004; 52 Kim (10.1016/j.neucom.2018.02.074_bib0038) 1997; 5 Krasnogor (10.1016/j.neucom.2018.02.074_bib0006) 2005; 9 Rojas (10.1016/j.neucom.2018.02.074_bib0037) 2002; 42 Cui (10.1016/j.neucom.2018.02.074_bib0023) 2015; 71 Yao (10.1016/j.neucom.2018.02.074_bib0032) 1996; 32 Ma (10.1016/j.neucom.2018.02.074_bib0028) 2017; 136 Zhao (10.1016/j.neucom.2018.02.074_bib0010) 2015; 42 Nielsen (10.1016/j.neucom.2018.02.074_bib0026) 2010 Zhongda (10.1016/j.neucom.2018.02.074_bib0049) 2017; 98 Tripathy (10.1016/j.neucom.2018.02.074_bib0003) 2015; 80 Shadmand (10.1016/j.neucom.2018.02.074_bib0004) 2016; 25 Li (10.1016/j.neucom.2018.02.074_bib0017) 2012; 23 Liu (10.1016/j.neucom.2018.02.074_bib0029) 2013; 45 Ho (10.1016/j.neucom.2018.02.074_bib0036) 2001; 9 da Silva (10.1016/j.neucom.2018.02.074_bib0020) 2016; 76 Zhang (10.1016/j.neucom.2018.02.074_bib0031) 1992; 3 Ardalani-Farsa (10.1016/j.neucom.2018.02.074_bib0046) 2011; 25 Almeida (10.1016/j.neucom.2018.02.074_bib0012) 2010; 73 Mousavi (10.1016/j.neucom.2018.02.074_bib0009) 2015; 36 Ebadat (10.1016/j.neucom.2018.02.074_bib0034) 2011; 16 Dhahri (10.1016/j.neucom.2018.02.074_bib0048) 2006 Sun (10.1016/j.neucom.2018.02.074_bib0007) 2014; 415 Wang (10.1016/j.neucom.2018.02.074_bib0039) 1992; 22 Gao (10.1016/j.neucom.2018.02.074_bib0030) 2017; 238 Chandra (10.1016/j.neucom.2018.02.074_bib0047) 2015; 26 Zhao (10.1016/j.neucom.2018.02.074_bib0015) 2007; 31 Takahashi (10.1016/j.neucom.2018.02.074_bib0025) 2014; 134 Ganjefar (10.1016/j.neucom.2018.02.074_bib0035) 2015; 294 Requena-Pérez (10.1016/j.neucom.2018.02.074_bib0014) 2006; 54 Mu (10.1016/j.neucom.2018.02.074_bib0008) 2015; 34 Takahashi (10.1016/j.neucom.2018.02.074_bib0001) 2016; 51 Cao (10.1016/j.neucom.2018.02.074_bib0022) 2015; 290 Tzeng (10.1016/j.neucom.2018.02.074_bib0033) 2010; 161 Rojas (10.1016/j.neucom.2018.02.074_bib0042) 2008; 71 Chen (10.1016/j.neucom.2018.02.074_bib0040) 2005; 174 da Silva (10.1016/j.neucom.2018.02.074_bib0019) 2016; 370–371 Li (10.1016/j.neucom.2018.02.074_bib0027) 2013; 117 Jang (10.1016/j.neucom.2018.02.074_bib0044) 1997 Melin (10.1016/j.neucom.2018.02.074_bib0043) 2012; 39 Kobayashi (10.1016/j.neucom.2018.02.074_bib0005) 2017; 260 Chandra (10.1016/j.neucom.2018.02.074_bib0045) 2012; 86 Kouda (10.1016/j.neucom.2018.02.074_bib0021) 2002; 16 Hu (10.1016/j.neucom.2018.02.074_bib0011) 2014; 25 Mirjalili (10.1016/j.neucom.2018.02.074_bib0013) 2012; 218 Cho (10.1016/j.neucom.2018.02.074_bib0041) 1996; 83 Martínez-Martínez (10.1016/j.neucom.2018.02.074_bib0002) 2015; 42 Shen (10.1016/j.neucom.2018.02.074_bib0024) 2008; 147 |
| References_xml | – volume: 80 start-page: 154 year: 2015 end-page: 158 ident: bib0003 article-title: Multiprocessor scheduling and neural network training methods using shuffled frog-leaping algorithm publication-title: Comput. Ind. Eng. – volume: 25 start-page: 15 year: 2014 end-page: 25 ident: bib0011 article-title: Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression publication-title: Appl. Soft Comput. – volume: 5 start-page: 523 year: 1997 end-page: 535 ident: bib0038 article-title: Forecasting time series with genetic fuzzy predictor ensemble publication-title: IEEE Trans. Fuzzy Syst. – volume: 39 start-page: 3494 year: 2012 end-page: 3506 ident: bib0043 article-title: A new approach for time series prediction using ensembles of ANFIS models publication-title: Expert Syst. Appl. – volume: 42 start-page: 3760 year: 2015 end-page: 3773 ident: bib0010 article-title: The memetic algorithm for the optimization of urban transit network publication-title: Expert Syst. Appl. – volume: 25 start-page: 45 year: 2011 end-page: 73 ident: bib0046 article-title: Residual analysis and combination of embedding theorem and artificial intelligence in chaotic time series forecasting publication-title: Appl. Artif. Intell. – volume: 29 start-page: 515 year: 1982 end-page: 546 ident: bib0018 article-title: Quantum mechanical Hamiltonian model of Turing machine publication-title: J. Stat. Phys. – volume: 76 start-page: 55 year: 2016 end-page: 64 ident: bib0020 article-title: Quantum perceptron over a field and neural network architecture selection in a quantum computer publication-title: Neural Netw. – volume: 51 start-page: 422 year: 2016 end-page: 430 ident: bib0001 article-title: Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data publication-title: Process. Biochem. – volume: 3 start-page: 889 year: 1992 end-page: 898 ident: bib0031 article-title: Wavelet networks publication-title: IEEE Trans. Neural Netw. – volume: 136 start-page: 140 year: 2017 end-page: 149 ident: bib0028 article-title: Research and application of quantum-inspired double parallel feed-forward neural network publication-title: Knowl.-Based Syst. – volume: 117 start-page: 81 year: 2013 end-page: 90 ident: bib0027 article-title: A hybrid quantum-inspired neural networks with sequence inputs publication-title: Neurocomputing – volume: 161 start-page: 2585 year: 2010 end-page: 2596 ident: bib0033 article-title: Design of fuzzy wavelet neural networks using GA approach for function approximation and system identification publication-title: Fuzzy Sets Syst. – volume: 42 start-page: 6433 year: 2015 end-page: 6441 ident: bib0002 article-title: An artificial neural network based expert system fitted with genetic algorithms for detecting the status of several rotary components in agro-industrial machines using a single vibration signal publication-title: Expert Syst. Appl. – volume: 45 start-page: 144 year: 2013 end-page: 150 ident: bib0029 article-title: Single-hidden-layer feed-forward quantum neural network based on grover learning publication-title: Neural Netw. – volume: 260 start-page: 174 year: 2017 end-page: 179 ident: bib0005 article-title: Gradient descent learning for quaternionic Hopfield neural networks publication-title: Neurocomputing – volume: 83 start-page: 325 year: 1996 end-page: 339 ident: bib0041 article-title: Radial basis function based adaptive fuzzy systems their applications to system identification and prediction publication-title: Fuzzy Sets Syst. – volume: 98 start-page: 158 year: 2017 end-page: 172 ident: bib0049 article-title: A prediction method based on wavelet transform and multiple models fusion for chaotic time series publication-title: Chaos, Solitons Fractals – volume: 36 start-page: 125 year: 2015 end-page: 142 ident: bib0009 article-title: A memetic algorithm applied to trajectory control by tuning of fractional order proportional-integral-derivative controllers publication-title: Appl. Soft Comput. – volume: 370–371 start-page: 120 year: 2016 end-page: 122 ident: bib0019 article-title: Comments on “quantum artificial neural networks with applications publication-title: Inf. Sci. – volume: 218 start-page: 11125 year: 2012 end-page: 11137 ident: bib0013 article-title: Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm publication-title: Appl. Math. Comput. – volume: 25 start-page: 12 year: 2016 end-page: 23 ident: bib0004 article-title: A new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimization publication-title: Biomed. Signal Process. Control – volume: 31 start-page: 1271 year: 2007 end-page: 1281 ident: bib0015 article-title: A mended hybrid learning algorithm for radial basis function neural networks to improve generalization capability publication-title: Appl. Math. Model. – volume: 23 start-page: 787 year: 2012 end-page: 799 ident: bib0017 article-title: Chaotic time series prediction based on a novel robust echo state network publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 147 start-page: 464 year: 2008 end-page: 469 ident: bib0024 article-title: Ammonia identification using shear horizontal surface acoustic wave sensor and quantum neural network model publication-title: Sens. Actuators A: Phys. – volume: 9 start-page: 200 year: 2001 end-page: 211 ident: bib0036 article-title: Fuzzy wavelet networks for function learning publication-title: IEEE Trans. Fuzzy Syst. – volume: 16 start-page: 3385 year: 2011 end-page: 3396 ident: bib0034 article-title: New fuzzy wavelet network for modeling and control: the modeling approach publication-title: Commun. Nonlinear Sci. Numer. Simul. – volume: 9 start-page: 474 year: 2005 end-page: 488 ident: bib0006 article-title: A tutorial for competent memetic algorithms: model, taxonomy, and design issues publication-title: IEEE Trans. Evol. Comput. – volume: 71 start-page: 11 year: 2015 end-page: 26 ident: bib0023 article-title: Complex rotation quantum dynamic neural networks (CRQDNN) using complex quantum neuron (CQN): Applications to time series prediction publication-title: Neural Netw. – volume: 134 start-page: 159 year: 2014 end-page: 164 ident: bib0025 article-title: Multi-layer quantum neural network controller trained by real-coded genetic algorithm publication-title: Neurocomputing – start-page: 2938 year: 2006 end-page: 2943 ident: bib0048 article-title: The modified differential evolution and the RBF (MDERBF) neural network for time series prediction publication-title: Proceedings of the International Joint Conference on Neural Networks – volume: 415 start-page: 261 year: 2014 end-page: 272 ident: bib0007 article-title: Fast computing global structural balance in signed networks based on memetic algorithm publication-title: Phys. A – volume: 86 start-page: 116 year: 2012 end-page: 123 ident: bib0045 article-title: Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction publication-title: Neurocomputing – volume: 294 start-page: 269 year: 2015 end-page: 285 ident: bib0035 article-title: Single-hidden-layer fuzzy recurrent wavelet neural network: applications to function approximation and system identification publication-title: Inf. Sci. – volume: 16 start-page: 67 year: 2002 end-page: 80 ident: bib0021 article-title: Image compression by layered quantum neural networks publication-title: Neural Process. Lett. – volume: 34 start-page: 485 year: 2015 end-page: 501 ident: bib0008 article-title: Memetic algorithm with simulated annealing strategy and tightness greedy optimization for community detection in networks publication-title: Appl. Soft Comput. – year: 2010 ident: bib0026 article-title: Quantum Computation and Quantum Information – volume: 26 start-page: 3123 year: 2015 end-page: 3136 ident: bib0047 article-title: Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 73 start-page: 1438 year: 2010 end-page: 1450 ident: bib0012 article-title: A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks publication-title: Neurocomputing – year: 1997 ident: bib0044 article-title: Neuro-Fuzzy and Soft Computing – volume: 22 start-page: 1414 year: 1992 end-page: 1427 ident: bib0039 article-title: Generating fuzzy rules by learning from examples publication-title: IEEE Trans. Syst. Man Cybern. – volume: 32 start-page: 360 year: 1996 end-page: 361 ident: bib0032 article-title: Evolving wavelet neural networks for function approximation publication-title: Electron. Lett. – volume: 54 start-page: 615 year: 2006 end-page: 624 ident: bib0014 article-title: Combined use of genetic algorithms and gradient descent optmization methods for accurate inverse permittivity measurement publication-title: IEEE Trans. Microwave Theory Tech. – volume: 290 start-page: 1 year: 2015 end-page: 6 ident: bib0022 article-title: Quantum artificial neural networks with applications publication-title: Inf. Sci. – volume: 52 start-page: 3409 year: 2004 end-page: 3416 ident: bib0016 article-title: Prediction of chaotic time series based on the recurrent predictor neural network publication-title: IEEE Trans. Signal Process. – volume: 238 start-page: 13 year: 2017 end-page: 23 ident: bib0030 article-title: Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis publication-title: Neurocomputing – volume: 71 start-page: 519 year: 2008 end-page: 537 ident: bib0042 article-title: Soft-computing techniques and ARMA model for time series prediction publication-title: Neurocomputing – volume: 42 start-page: 267 year: 2002 end-page: 285 ident: bib0037 article-title: Time series analysis using normalized PG-RBF network with regression weights publication-title: Neurocomputing – volume: 174 start-page: 219 year: 2005 end-page: 235 ident: bib0040 article-title: Time-series forecasting using flexible neural tree model publication-title: Inf. Sci. – volume: 5 start-page: 523 year: 1997 ident: 10.1016/j.neucom.2018.02.074_bib0038 article-title: Forecasting time series with genetic fuzzy predictor ensemble publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/91.649903 – volume: 25 start-page: 12 year: 2016 ident: 10.1016/j.neucom.2018.02.074_bib0004 article-title: A new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimization publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2015.10.008 – volume: 218 start-page: 11125 year: 2012 ident: 10.1016/j.neucom.2018.02.074_bib0013 article-title: Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2012.04.069 – volume: 31 start-page: 1271 year: 2007 ident: 10.1016/j.neucom.2018.02.074_bib0015 article-title: A mended hybrid learning algorithm for radial basis function neural networks to improve generalization capability publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2006.04.014 – volume: 136 start-page: 140 year: 2017 ident: 10.1016/j.neucom.2018.02.074_bib0028 article-title: Research and application of quantum-inspired double parallel feed-forward neural network publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2017.09.013 – volume: 370–371 start-page: 120 year: 2016 ident: 10.1016/j.neucom.2018.02.074_bib0019 article-title: Comments on “quantum artificial neural networks with applications publication-title: Inf. Sci. doi: 10.1016/j.ins.2016.07.062 – volume: 98 start-page: 158 year: 2017 ident: 10.1016/j.neucom.2018.02.074_bib0049 article-title: A prediction method based on wavelet transform and multiple models fusion for chaotic time series publication-title: Chaos, Solitons Fractals doi: 10.1016/j.chaos.2017.03.018 – volume: 16 start-page: 67 year: 2002 ident: 10.1016/j.neucom.2018.02.074_bib0021 article-title: Image compression by layered quantum neural networks publication-title: Neural Process. Lett. doi: 10.1023/A:1019708909383 – volume: 134 start-page: 159 year: 2014 ident: 10.1016/j.neucom.2018.02.074_bib0025 article-title: Multi-layer quantum neural network controller trained by real-coded genetic algorithm publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.12.073 – volume: 42 start-page: 6433 year: 2015 ident: 10.1016/j.neucom.2018.02.074_bib0002 article-title: An artificial neural network based expert system fitted with genetic algorithms for detecting the status of several rotary components in agro-industrial machines using a single vibration signal publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2015.04.018 – volume: 32 start-page: 360 year: 1996 ident: 10.1016/j.neucom.2018.02.074_bib0032 article-title: Evolving wavelet neural networks for function approximation publication-title: Electron. Lett. doi: 10.1049/el:19960229 – volume: 54 start-page: 615 year: 2006 ident: 10.1016/j.neucom.2018.02.074_bib0014 article-title: Combined use of genetic algorithms and gradient descent optmization methods for accurate inverse permittivity measurement publication-title: IEEE Trans. Microwave Theory Tech. doi: 10.1109/TMTT.2005.862671 – volume: 71 start-page: 11 year: 2015 ident: 10.1016/j.neucom.2018.02.074_bib0023 article-title: Complex rotation quantum dynamic neural networks (CRQDNN) using complex quantum neuron (CQN): Applications to time series prediction publication-title: Neural Netw. doi: 10.1016/j.neunet.2015.07.013 – volume: 80 start-page: 154 year: 2015 ident: 10.1016/j.neucom.2018.02.074_bib0003 article-title: Multiprocessor scheduling and neural network training methods using shuffled frog-leaping algorithm publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2014.12.013 – volume: 260 start-page: 174 year: 2017 ident: 10.1016/j.neucom.2018.02.074_bib0005 article-title: Gradient descent learning for quaternionic Hopfield neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.04.025 – volume: 23 start-page: 787 year: 2012 ident: 10.1016/j.neucom.2018.02.074_bib0017 article-title: Chaotic time series prediction based on a novel robust echo state network publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2012.2188414 – volume: 161 start-page: 2585 year: 2010 ident: 10.1016/j.neucom.2018.02.074_bib0033 article-title: Design of fuzzy wavelet neural networks using GA approach for function approximation and system identification publication-title: Fuzzy Sets Syst. doi: 10.1016/j.fss.2010.06.002 – volume: 86 start-page: 116 year: 2012 ident: 10.1016/j.neucom.2018.02.074_bib0045 article-title: Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.01.014 – volume: 52 start-page: 3409 year: 2004 ident: 10.1016/j.neucom.2018.02.074_bib0016 article-title: Prediction of chaotic time series based on the recurrent predictor neural network publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2004.837418 – volume: 9 start-page: 474 year: 2005 ident: 10.1016/j.neucom.2018.02.074_bib0006 article-title: A tutorial for competent memetic algorithms: model, taxonomy, and design issues publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2005.850260 – volume: 76 start-page: 55 year: 2016 ident: 10.1016/j.neucom.2018.02.074_bib0020 article-title: Quantum perceptron over a field and neural network architecture selection in a quantum computer publication-title: Neural Netw. doi: 10.1016/j.neunet.2016.01.002 – volume: 117 start-page: 81 year: 2013 ident: 10.1016/j.neucom.2018.02.074_bib0027 article-title: A hybrid quantum-inspired neural networks with sequence inputs publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.01.029 – volume: 25 start-page: 45 year: 2011 ident: 10.1016/j.neucom.2018.02.074_bib0046 article-title: Residual analysis and combination of embedding theorem and artificial intelligence in chaotic time series forecasting publication-title: Appl. Artif. Intell. doi: 10.1080/08839514.2011.529263 – volume: 415 start-page: 261 year: 2014 ident: 10.1016/j.neucom.2018.02.074_bib0007 article-title: Fast computing global structural balance in signed networks based on memetic algorithm publication-title: Phys. A doi: 10.1016/j.physa.2014.07.071 – volume: 294 start-page: 269 year: 2015 ident: 10.1016/j.neucom.2018.02.074_bib0035 article-title: Single-hidden-layer fuzzy recurrent wavelet neural network: applications to function approximation and system identification publication-title: Inf. Sci. doi: 10.1016/j.ins.2014.09.054 – volume: 16 start-page: 3385 year: 2011 ident: 10.1016/j.neucom.2018.02.074_bib0034 article-title: New fuzzy wavelet network for modeling and control: the modeling approach publication-title: Commun. Nonlinear Sci. Numer. Simul. doi: 10.1016/j.cnsns.2010.12.011 – volume: 9 start-page: 200 year: 2001 ident: 10.1016/j.neucom.2018.02.074_bib0036 article-title: Fuzzy wavelet networks for function learning publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/91.917126 – year: 1997 ident: 10.1016/j.neucom.2018.02.074_bib0044 – volume: 290 start-page: 1 year: 2015 ident: 10.1016/j.neucom.2018.02.074_bib0022 article-title: Quantum artificial neural networks with applications publication-title: Inf. Sci. doi: 10.1016/j.ins.2014.08.033 – start-page: 2938 year: 2006 ident: 10.1016/j.neucom.2018.02.074_bib0048 article-title: The modified differential evolution and the RBF (MDERBF) neural network for time series prediction – volume: 174 start-page: 219 year: 2005 ident: 10.1016/j.neucom.2018.02.074_bib0040 article-title: Time-series forecasting using flexible neural tree model publication-title: Inf. Sci. doi: 10.1016/j.ins.2004.10.005 – volume: 71 start-page: 519 year: 2008 ident: 10.1016/j.neucom.2018.02.074_bib0042 article-title: Soft-computing techniques and ARMA model for time series prediction publication-title: Neurocomputing doi: 10.1016/j.neucom.2007.07.018 – volume: 29 start-page: 515 year: 1982 ident: 10.1016/j.neucom.2018.02.074_bib0018 article-title: Quantum mechanical Hamiltonian model of Turing machine publication-title: J. Stat. Phys. doi: 10.1007/BF01342185 – volume: 26 start-page: 3123 year: 2015 ident: 10.1016/j.neucom.2018.02.074_bib0047 article-title: Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2015.2404823 – volume: 39 start-page: 3494 year: 2012 ident: 10.1016/j.neucom.2018.02.074_bib0043 article-title: A new approach for time series prediction using ensembles of ANFIS models publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.09.040 – volume: 238 start-page: 13 year: 2017 ident: 10.1016/j.neucom.2018.02.074_bib0030 article-title: Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.01.032 – volume: 42 start-page: 3760 year: 2015 ident: 10.1016/j.neucom.2018.02.074_bib0010 article-title: The memetic algorithm for the optimization of urban transit network publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.11.056 – volume: 34 start-page: 485 year: 2015 ident: 10.1016/j.neucom.2018.02.074_bib0008 article-title: Memetic algorithm with simulated annealing strategy and tightness greedy optimization for community detection in networks publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.05.034 – volume: 25 start-page: 15 year: 2014 ident: 10.1016/j.neucom.2018.02.074_bib0011 article-title: Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2014.09.007 – year: 2010 ident: 10.1016/j.neucom.2018.02.074_bib0026 – volume: 45 start-page: 144 year: 2013 ident: 10.1016/j.neucom.2018.02.074_bib0029 article-title: Single-hidden-layer feed-forward quantum neural network based on grover learning publication-title: Neural Netw. doi: 10.1016/j.neunet.2013.02.012 – volume: 36 start-page: 125 year: 2015 ident: 10.1016/j.neucom.2018.02.074_bib0009 article-title: A memetic algorithm applied to trajectory control by tuning of fractional order proportional-integral-derivative controllers publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.08.009 – volume: 147 start-page: 464 year: 2008 ident: 10.1016/j.neucom.2018.02.074_bib0024 article-title: Ammonia identification using shear horizontal surface acoustic wave sensor and quantum neural network model publication-title: Sens. Actuators A: Phys. doi: 10.1016/j.sna.2008.05.025 – volume: 3 start-page: 889 year: 1992 ident: 10.1016/j.neucom.2018.02.074_bib0031 article-title: Wavelet networks publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.165591 – volume: 22 start-page: 1414 year: 1992 ident: 10.1016/j.neucom.2018.02.074_bib0039 article-title: Generating fuzzy rules by learning from examples publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/21.199466 – volume: 42 start-page: 267 year: 2002 ident: 10.1016/j.neucom.2018.02.074_bib0037 article-title: Time series analysis using normalized PG-RBF network with regression weights publication-title: Neurocomputing doi: 10.1016/S0925-2312(01)00338-1 – volume: 51 start-page: 422 year: 2016 ident: 10.1016/j.neucom.2018.02.074_bib0001 article-title: Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data publication-title: Process. Biochem. doi: 10.1016/j.procbio.2015.12.005 – volume: 73 start-page: 1438 year: 2010 ident: 10.1016/j.neucom.2018.02.074_bib0012 article-title: A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2009.11.007 – volume: 83 start-page: 325 year: 1996 ident: 10.1016/j.neucom.2018.02.074_bib0041 article-title: Radial basis function based adaptive fuzzy systems their applications to system identification and prediction publication-title: Fuzzy Sets Syst. doi: 10.1016/0165-0114(95)00322-3 |
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| Title | Optimization of quantum-inspired neural network using memetic algorithm for function approximation and chaotic time series prediction |
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