Plane problem is a typical combinatorial optimization problem. Aiming at the difference method of plane problem, BP neural network is proposed, the algorithm of solving difference equation is established, and the corresponding program is compiled. By calculating the calculation example, the continuity condition under the condition of modulus abruption is further discussed. The correctness and practicability of the difference equation algorithm are verified. A dynamic model of the parallel difference equation is constructed according to the characteristics of the parallel structure of BP neural network. The study shows that the two groups of differential equations are used to identify and verify the model, and the energy function that satisfies both the linear embedding condition and the correct wiring is given. Furthermore, BP neural network is used to realize the search and routing of the maximum plane subgraph of the planable line and the non-planar plan. The results show that the verification used is effective. Difference equation calculations have the ability to help BP networks get rid of local minima and get better results.
ShiH., XieS. and WangX., A warpage optimization method for injection molding using artificial neural network with parametric sampling evaluation strategy, International Journal of Advanced Manufacturing Technology65 (2013), 343–353.
2.
SinghG. and WaliaB.S., Performance evaluation of nature-inspired algorithms for the design of bored pile foundation by artificial neural networks, Neural Computing & Applications13 (2016), 1–10.
3.
SilvaS.F., AnjosC.A.R., CavalcantiR.N., et al., Evaluation of extra virgin olive oil stability by artificial neural network, Food Chemistry179 (2015), 35–43.
4.
ZhangY., YouD., GaoX., et al., Automatic gap tracking during high power laser welding based on particle filtering method and BP neural network, International Journal of Advanced Manufacturing Technology96 (2018), 685–696.
5.
SamtaşG., Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network, The International Journal of Advanced Manufacturing Technology73 (2014), 353–364.
6.
YouX. and CaoX., Study of Liquid Lithium Coolant Interaction Based on BP Neural Network Optimized by Genetic Algorithm, Journal of Fusion Energy34(4) (2015), 918–924.
7.
SilvaA.S., SantosR.C.D., BotturaF.B., et al., Development and evaluation of a prototype for remote voltage monitoring based on artificial neural networks, Engineering Applications of Artificial Intelligence57 (2017), 50–60.
8.
SodeifianG., SajadianS.A. and ArdestaniN.S., Evaluation of the response surface and hybrid artificial neural network-genetic algorithm methodologies to determine extraction yield of Ferulago angulata through supercritical fluid, Journal of the Taiwan Institute of Chemical Engineers60 (2016), 165–173.
9.
BetikuE., OkunsolawoS.S., AjalaS.O., et al., Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter, Renewable Energy76 (2015), 408–417.
10.
Tong-TongW., JianZ., ChuanT.U., et al., Application of IPSO-BP Neural Network in Water Quality Evaluation for Tianshui Section of Wei River, Environmental Science & Technology36(8) (2013), 175–181.
11.
MonjeziM., MehrdaneshA., MalekA., et al., Evaluation of effect of blast design parameters on flyrock using artificial neural networks, Neural Computing & Applications23(2) (2013), 349–356.
12.
HirotaM., FukuiS., OkamotoK., et al., Evaluation of combinations of sensitization test descriptors for the artificial neural network-based risk assessment model of skin sensitization: Evaluation of the descriptor for ANN risk assessment of skin sensitization, Journal of Applied Toxicology35(11) (2015), 1333–1347.
13.
FanX., FeiZ., HeQ., et al., Evaluation of driver fatigue with multi-indicators based on Artificial Neural Network, Iet Intelligent Transport Systems10(8) (2016), 555–561.
14.
Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis, Neural Computing and Applications27(2) (2016), 375–385.
15.
ShafabakhshG.A., TalebsafaM., MotamediM., et al., Analytical evaluation of load movement on flexible pavement and selection of optimum neural network algorithm, KSCE Journal of Civil Engineering19(6) (2015), 1738–1746.
16.
MansouriM., GolsefidM.T. and NematbakhshN., A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection, Archives of Medical Research44(4) (2015), 266–272.
17.
XueX. and XiaoauthorM., Application of genetic algorithm-based support vector machines for prediction of soil liquefaction, Environmental Earth Sciences75(10) (2016), 1–11.
18.
GusevE.M., AyzelG.V. and NasonovaO.N., Runoff evaluation for ungauged watersheds by SWAP model. 1. Application of artificial neural networks, Water Resources44(2) (2017), 169–179.
19.
AhmedR., El SayedM., GadsdenS.A., et al., Automotive Internal-Combustion-Engine Fault Detection and Classification Using Artificial Neural Network Techniques, IEEE Transactions on Vehicular Technology64(1) (2015), 21–33.
20.
LiuX.H., ShanM.Y. and ZhangL.H., Low-carbon supply chain resources allocation based on quantum chaos neural network algorithm and learning effect, Natural Hazards83(1) (2016), 389–409.
21.
ZhaoJ., DingH., ZhaoW., et al., Modelling of the hot deformation behaviour of a titanium alloy using constitutive equations and artificial neural network, Computational Materials Science92(5) (2014), 47–56.
22.
ChangX.L., MiX.M. and MuppalaJ.K., Performance evaluation of artificial intelligence algorithms for virtual network embedding, Engineering Applications of Artificial Intelligence26(10) (2013), 2540–2550.
23.
NairV.V., DharH., KumarS., et al., Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor, , Bioresource Technology217 (2016), 90–99.
24.
KowalskiP.A. and ŁukasikS., Training Neural Networks with Krill Herd Algorithm, Neural Processing Letters44(1) (2016), 5–17.
25.
LiuY., HuangH.Z. and LingD., Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment, International Journal of Systems Science44(3) (2013), 545–555.
26.
BayramA., UzluE., KankalM., et al., Modeling stream dissolved oxygen concentration using teaching– learning based optimization algorithm, Environmental Earth Sciences73(10) (2015), 6565–6576.
27.
RobertoA.C., Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks, Anais Da Academia Brasileira De Ciencias85 (2013), 1523–1535.
28.
KrishnarajN., ElhosenyMohamed, ThenmozhiM., SelimMahmoud M. and ShankarK., “Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)”, Journal of Real-Time Image Processing (2019). In Press. 10.1007/s11554-019-00879-6.
29.
ElhosenyMohamed and ShankarK., “Optimal Bilateral Filter and Convolutional Neural Network based Denoising Method of Medical Image Measurements”, Measurement143 (2019), 125–135.
30.
MuruganB.S., ElhosenyMohamed, ShankarK. and UthayakumarJ., “Region-based scalable smart system for anomaly detection in pedestrian walkways”, Computers and Electrical Engineering75 (2019), 146–160.
31.
LakshmanaprabuS.K., ShankarK., AshishK., DeepakG., JoelJ.P.C., Rodrigues, PinheiroPlácido R. and de AlbuquerqueVictor Hugo C., “Effective Features to Classify Big Data using Social Internet of Things”, IEEE Access6 (2018), 24196–24204.
