Crop classification by forward neural network with adaptive chaotic particle swarm optimization

Sensors

Volumn 11, Issue 5, 2011, Pages 4721-4743

  Zhang, Yudong ^a   Wu, Lenan ^a  

^a SOUTHEAST UNIVERSITY   (China)

Author keywords

Artificial neural network; Particle swarm optimization; Principle component analysis; Synthetic aperture radar

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CROP; GENETIC PROCEDURES; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS;

ALGORITHMS; BIOSENSING TECHNIQUES; CROPS, AGRICULTURAL; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS;

EID: 79957756950     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s110504721     Document Type: Article

References (45)

1. Y.A. Impact of the plasma fluctuations in the Martian ionosphere on the performance of the synthetic aperture ground-penetrating radar. Planet. Space Sci. 2009, 57, 1458-1466.
2. Pal, S.K.; Majumdar, T.J.; Bhattacharya, A.K. ERS-2 SAR and IRS-1C LISS III data fusion: A PCA approach to improve remote sensing based geological interpretation. ISPRS J. Photogramm. Remote Sens. 2007, 61, 281-297.
3. Khan, K.U.; Yang, J. Polarimetric synthetic aperture radar image classification by a hybrid method. Tsinghua Sci. Technol. 2007, 12, 97-104.
4. Simons, M.; Rosen, P.A. Interferometric synthetic aperture radar geodesy. In Treatise on Geophysics; Gerald, S., Ed.; Elsevier: Amsterdam, The Netherlands, 2007; pp. 391-446.
5. Hellmann, M.; Jäger, G. Fuzzy rule based classification of polarimetric SAR data. Aerosp. Sci. Technol. 2002, 6, 217-232.
6. Gadelmawla, E.S. A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDTE Int. 2004, 37, 577-588.
7. Quan, J.J.; Wen, X.B.; Xu, X.Q. Multiscale probabilistic neural network method for SAR image segmentation. Appl. Math. Comput. 2008, 205, 578-583.
8. Fontenla-Romero, O.; Guijarro-Berdiñas, B.; Pérez-Sánchez, B.; Alonso-Betanzos, A. A new convex objective function for the supervised learning of single-layer neural networks. Patt. Recog. 2010, 43, 1984-1992.
9. Mansour, M.A.A.; Dessouky, M.M. A genetic algorithm approach for solving the daily photograph selection problem of the SPOT5 satellite. Comput. Ind. Eng. 2010, 58, 509-520.
10. Fernández, A.; Gómez, S. Portfolio selection using neural networks. Comput. Oper. Res. 2007, 34, 1177-1191.
11. Zhang, Y.; Yan, J.; Wei, G.; Wu, L. Find multi-objective paths in stochastic networks via chaotic immune PSO. Expert Syst. Appl. 2010, 37, 1911-1919.
12. Zhang, Y.D.; Wu, L. Weights optimization of neural network via improved BCO approach. Prog. Electromagn. Res. 2008, 83, 185-198.
13. Polat, K.; Günes, S. A new method to forecast of Escherichia coli promoter gene sequences: Integrating feature selection and Fuzzy-AIRS classifier system. Expert Syst. Appl. 2009, 36, 57-64.
14. Delen, D.; Walker, G.; Kadam, A. Predicting breast cancer survivability: A comparison of three data mining methods. Artif. Intell. Med. 2005, 34, 113-127.
15. Kertész-Farkas, A.; Dhir, S.; Sonego, P.; Pacurar, M.; Netoteia, S.; Nijveen, H.; Kuzniar, A.; Leunissen, J.A.M.; Kocsor, A.; Pongor, S. Benchmarking protein classification algorithms via supervised cross-validation. J. Biochem. Biophys. Meth. 2008, 70, 1215-1223.
16. Jouan, A.; Allard, Y. Land use mapping with evidential fusion of features extracted from polarimetric synthetic aperture radar and hyperspectral imagery. Inform. Fusion 2004, 5, 251-267.
17. Goodarzi, M.; Freitas, M.P. MIA-QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives. Eur. J. Med. Chem. 2010, 45, 1352-1358.
18. Zhang, Y.; Wu, L. Pattern recognition via PCNN and Tsallis entropy. Sensors 2008, 8, 7518-7529.
19. Celik, T. A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images. Signal Process. 2010, 90, 1471-1485.
20. Alvarenga, A.V.; Teixeira, C.A.; Ruano, M.G.; Pereira, W.C.A. Influence of temperature variations on the entropy and correlation of the Grey-Level Co-occurrence Matrix from B-Mode images. Ultrasonics 2010, 50, 290-293.
21. Werth, M.T.; Halouska, S.; Shortridge, M.D.; Zhang, B.; Powers, R. Analysis of metabolomic PCA data using tree diagrams. Analytical Biochem. 2010, 399, 58-63.
22. Korte, F.S.; Razumova, M.V.; Feest, E.R.; Wang, D.; Regnier, M. PKA phosphorylation has no effect on the force-PCA relationship or length dependent activation following L48Q cTNC-Tn exchange in rat demembranated trabeculae. Biophys. J. 2010, 98, (Supplement 1), 6a.
23. Zhang, Y.; Wu, L. Stock Market Prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Syst. Appl. 2009, 36, 8849-8854.
24. Radulovic, J.; Rankovic, V. Feedforward neural network and adaptive network-based fuzzy inference system in study of power lines. Expert Syst. Appl. 2010, 37, 165-170.
25. de Carvalho, A.B.; Pozo, A.; Vergilio, S.R. A symbolic fault-prediction model based on multiobjective particle swarm optimization. J. Syst. Softw. 2010, 83, 868-882.
26. Niknam, T.; Mojarrad, H.D.; Nayeripour, M. A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch. Energy 2010, 35, 1764-1778.
27. Bonilla-Petriciolet, A.; Segovia-Hernández, J.G. A comparative study of particle swarm optimization and its variants for phase stability and equilibrium calculations in multicomponent reactive and non-reactive systems. Fluid Phase Equilibria 2010, 289, 110-121.
28. dos Santos Coelho, L.; Coelho, A.A.R. Model-free adaptive control optimization using a chaotic particle swarm approach. Chaos. Soliton. Fractal. 2009, 41, 2001-2009.
29. Wang, J.; Zhu, S.; Zhao, W.; Zhu, W. Optimal parameters estimation and input subset for grey model based on chaotic particle swarm optimization algorithm. Expert Syst. Appl. 2011, 38, 8151-8158.
30. Chuang, L.Y.; Tsai, S.W.; Yang, C.H. Chaotic catfish particle swarm optimization for solving global numerical optimization problems. Appl. Math. Comput. 2011, 217, 6900-6916.
31. Araujo, E.; Coelho, L.d.S. Particle swarm approaches using Lozi map chaotic sequences to fuzzy modelling of an experimental thermal-vacuum system. Appl. Soft Comput. 2008, 8, 1354-1364.
32. L.d.S. An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications. Reliab. Eng. Syst. Safety 2009, 94, 830-837.
33. Coelho, L.d.S.; Mariani, V.C. Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects. Energ. Conv. Manage. 2008, 49, 3080-3085.
34. Cai, J.; Ma, X.; Li, Q.; Li, L.; Peng, H. A multi-objective chaotic particle swarm optimization for environmental/economic dispatch. Energ. Conv. Manage. 2009, 50, 1318-1325.
35. Coelho, L.d.S.; Sauer, J.G.; Rudek, M. Differential evolution optimization combined with chaotic sequences for image contrast enhancement. Chaos. Soliton. Fractal. 2009, 42, 522-529.
36. Sun, J.; Zhao, J.; Wu, X.; Fang, W.; Cai, Y.; Xu, W. Parameter estimation for chaotic systems with a drift particle swarm optimization method. Phys. Lett. A 2010, 374, 2816-2822.
37. Zhang, Y.; Wu, L.; Wang, S.; Huo, Y. Chaotic Artificial bee colony used for cluster analysis. Commun. Comput. Inform. Sci. 2011, 134, 205-211.
38. Affleck, R.T.; Melloh, R.A.; Shoop, S.A. Cross-country mobility on various snow conditions for validation of a virtual terrain. J. Terramech. 2009, 46, 203-210.
39. Pradhan, B. Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv. Space Res. 2010, 45, 1244-1256.
40. Kandrika, S.; Roy, P.S. Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach. Int. J. Appl. Earth Obs. Geoinf. 2008, 10, 186-193.
41. Zhang, Y.; Wu, L.; Wei, G. A new classifier for polarimetric SAR images. Prog. Electromagn. Res. 2009, 94, 83-104.
42. Yuan, H.C.; Xiong, F.L.; Huai, X.Y. A method for estimating the number of hidden neurons in feed-forward neural networks based on information entropy. Comput. Electron. Agric. 2003, 40, 57-64.
43. Majdi, A.; Beiki, M. Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. Int. J. Rock Mech. Mining Sci. 2010, 47, 246-253.
44. Toprak, A.; Siraç Özerdem, M.; Güler, I. Suppression of impulse noise in MR images using artificial intelligent based neuro-fuzzy adaptive median filter. Digit. Signal Process. 2008, 18, 391-405.
45. Hamidian, D.; Seyedpoor, S.M. Shape optimal design of arch dams using an adaptive neuro-fuzzy inference system and improved particle swarm optimization. Appl. Math. Model. 2010, 34, 1574-1585.