An improved approximation approach incorporating particle swarm optimization and a priori information into neural networks

Neural Computing and Applications

Volumn 19, Issue 2, 2010, Pages 255-261

  Han, Fei ^a   Ling, Qing Hua ^b   Huang, De Shuang ^c  

^a JIANGSU UNIVERSITY   (China)

^b JIANGSU UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c INSTITUTE OF INTELLIGENT MACHINES   (China)

Author keywords

A priori information; Approximation accuracy; Covergence rate; Function approximation; Particle swarm optimization

Indexed keywords

ADAPTIVE PARTICLE SWARM OPTIMIZATIONS; APPROXIMATION ACCURACY; FUNCTION APPROXIMATION; FUNCTION APPROXIMATION PROBLEMS; GRADIENT BASED ALGORITHM; GRADIENT-BASED LEARNING; MAGNIFIED GRADIENT FUNCTIONS; PRIORI INFORMATION;

FEEDFORWARD NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO);

APPROXIMATION ALGORITHMS;

EID: 84874021789     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-009-0274-y     Document Type: Article

References (26)

1. Nasr MB, Chtourou M (2009) A fuzzy neighborhood-based training algorithm for feedforward neural networks. Neural Comput Appl 18(2):127-133. doi:10.1007/s00521-007-0165-z
2. Huang DS (2004) A constructive approach for finding arbitrary roots of polynomials by neural networks. IEEE Trans Neural Netw 15:477-491. doi:10.1109/TNN.2004.824424
3. Huang DS, Chi ZR (2004) Finding roots of arbitrary high order polynomials based on neural network recursive partitioning method. Sci China Ser Inf Sci 47:232-245
4. Huang DS, Horace IHS, Chi ZR (2004) A neural root finder of polynomials based on root momnets. Neural Comput 16:1721-1762. doi:10.1162/089976604774201668
5. Huang DS, Horace HS Ip, Chi ZR, Wong HS (2003) Dilation method for finding close roots of polynomials based on constrained learning neural networks. Phys Lett A 309:443-451. doi: 10.1016/S0375-9601(03)00216-0
6. Han F, Huang DS (2008) A new constrained learning algorithm for function approximation by encoding a priori information into feedforward neural networks. Neural Comput Appl 17(5-6):433-439
7. Li SG, Wu ZM (2008) Business performance forecasting of convenience store based on enhanced fuzzy neural network. Neural Comput Appl 17(5-6):569-578
8. Jeong SY, Lee SY (2000) Adaptive learning algorithms to incorporate additional functional constraints into neural networks. Neurocomputing 35:73-90. doi:10.1016/S0925-2312(00) 00296-4
9. Jeong DG, Lee SY (1996) Merging back-propagation and Hebbian learning rules for robust classifications. Neural Netw 9:1213-1222. doi:10.1016/0893-6080(96)00042-1
10. Han F, Huang DS, Cheung YM, Huang GB (2005) A new modified hybrid learning algorithm for feedforward neural networks, vol 3496. In: International symposium on neural network, Chongqing, 30 May-1 June, China. Lecture Notes in Computer Science, Springer, Berlin, pp 572-577
11. Eberhart RC, Kennedy J (1995) A new optimizer using particles swarm theory. In: Proceeding of sixth international symposium on micro machine and human science, Nagoya, Japan, pp 39-43
12. Eberhart RC, Kennedy J (1995) Particle swarm optimization, proceeding of IEEE International Conference on Neural Network, Perth, Australia, pp 1942-1948
13. Parrott D, Li XD (2006) Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans Evol Comput 10(4):440-458. doi:10.1109/TEVC.2005.859468
14. Blackwell T, Branke J (2006) Multiswarms, exclusion, and anticonvergence in dynamic environments. IEEE Trans Evol Comput 10(4):459-472. doi:10.1109/TEVC.2005.857074
15. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58-73. doi:10.1109/4235.985692
16. Parsopoulos K, Vrahatis M (2002) Recent approaches to global optimization problems through particle swarm optimization. Nat Comput 1(2-3):235-306. doi:10.1023/A:1016568309421
17. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading
18. Langdon WB, Poli R (2007) Evolving problems to learn about particle swarm optimizers and other search algorithms. IEEE Trans Evol Comput 11(5):561-578. doi:10.1109/TEVC.2006. 886448
19. Wang YP, Dang CY (2007) An evolutionary algorithm for global optimization based on level-set evolution and latin squares. IEEE Trans Evol Comput 11(5):579-595. doi:10.1109/TEVC. 2006.886802
20. Han F, Ling QH (2008) A new approach for function approximation incorporating adaptive particle swarm optimization and a priori information. Appl Math Comput 205(2):792-798. doi: 10.1016/j.amc.2008.05.025
21. Ng SC, Cheung CC, Leung SH (2004) Magnified gradient function with deterministic weight modification in adaptive learning. IEEE Trans Neural Netw 15(6):1411-1423. doi:10. 1109/TNN.2004.836237
22. Shi YH, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of IEEE world conference on computation intelligence, pp 69-73
23. Shi YH, Eberhart RC (1998) Parameter selection in particle swarm optimization. In: 1998 annual conference on evolutionary programming, San Diego, March
24. Funahashi K (1989) On the approximate realization of continuous mapping by neural networks. Neural Netw 2(3):183-192. doi: 10.1016/0893-6080(89)90003-8
25. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal, approximators. Neural Netw 2(5):359-366. doi:10.1016/0893-6080(89)90020-8
26. Irie B, Miyake S (1988) Capabilities of three-layered perceptions. In: Proceedings of the IEEE conference on neural networks, vol I. San Diego, CA, pp 641-648