论文信息 - Neuro-fuzzy support for problem-solving environments: a step toward automated solution of PDEs

Neuro-fuzzy support for problem-solving environments: a step toward automated solution of PDEs

In the "problem-solving environments" that will one day free scientists and engineers from many algorithmic and computing details, computational intelligence techniques such as neural networks and fuzzy logic can help automate the choice of the best solution methods. Classifying the problems, illustrated in this article by using partial differential equations (PDEs) as an example, is a first step.

John R. Rice | Elias N. Houstis | Anupam Joshi | Sanjiva Weerawarana | Narendran Ramakrishnan

[1] E. Gallopoulos,et al. Computer as thinker/doer: problem-solving environments for computational science , 1994, IEEE Computational Science and Engineering.

[2] Patrick K. Simpson,et al. Fuzzy min-max neural networks. I. Classification , 1992, IEEE Trans. Neural Networks.

[3] Andreas Zell,et al. SNNS (Stuttgart Neural Network Simulator) , 1994 .

[4] Martin A. Riedmiller,et al. A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[5] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .

[6] John R. Rice,et al. //ELLPACK: a numerical simulation programming environment for parallel MIMD machines , 1990, ICS '90.

[7] Elias N. Houstis,et al. The use of neural networks to support "intelligent" scientific computing , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[8] P. K. Simpson. Fuzzy Min-Max Neural Networks-Part 1 : Classification , 1992 .

[9] John R. Rice,et al. A Population of Linear, Second Order, Elliptic Partial Differential Equations on Rectangular Domains. Part 1 , 1981 .