In most of experimental research, we generally need to solve a problem of classification, regression or time-series forecasting. On the other hand, artificial neural networks are universal and highlydoi:10.1007/978-3-319-28419-4_16Wojciech Salabun...
Solution to inverse problems is of interest in many fields of science and engineering. In nondestructive evaluation [1], for example, inverse techniques are used to obtain quantitative estimates of the size, shape and nature of defects in materials. Inv.
Gradient calculations for dynamic recurrent neural networks: A survey IEEE Transactions on Neural Networks, 6 (5) (1995), pp. 1212-1228 View in ScopusGoogle Scholar 9. S.H. Zak, V. Upatising, S. Hui Solving linear programming problems with neural networks: A comparative study IEEE Transacti...
SOLVING COMBINATORIAL OPTIMISATION PROBLEMS USING NEURAL NETWORKS Over the last decade or so, two maintypes of neural networks have been proposed for solving COP's- in particular, the Travelling Salesman Problem (TSP). The rst of these neural approaches is the Hop eld neural network which ... ...
最常用的函数有多项式函数(polynomial functions)、傅里叶函数(Fourier functions)、径向基函数(radial basis functions)和人工神经网络(artificial neural networks)。参数化策略有几个优点:(1)具有更高的计算效率,在高维或连续空间中需要更少的存储内存;以及(2)一些特殊的参数化选择可以提供将先验知识添加到策略中的好...
I don't recommend working through all the problems. What's even better is to find your own project. Maybe you want to use neural nets to classify your music collection. Or to predict stock prices. Or whatever. Butfind a project you care about. Then you can ignore the problems in the ...
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In terms of algorithms, the genetic algorithm9 and library approach10 stand out with their understandability and feasibility. However, the existing algorithms are usually time-consuming due to the global optimization of a huge parameter space. Recently, neural networks11,12 (NNs) have offered a ...
RI 02912e-mail: george_karniadakis@brown.eduPhysics-Informed NeuralNetworks for Heat TransferProblemsPhysics-informed neural networks (PINNs) have gained popularity across different engi-neering f i elds due to their effectiveness in solving realistic problems with noisy data andoften partially missing ...
Vladimir I Gorbachenko, Tatiana V Lazovskaya, Dmitriy A Tarkhov, Alexander N Vasilyev, and Maxim V Zhukov. Neural network technique in some inverse problems of mathematical physics. In International Symposium on Neural Networks, pages 310-316. Springer, 2016....