Uncertainty, one of whose most important forms is fuzziness, exists universally in the objective and subjective world. Artificial Intelligence with uncertainty, the advanced development of artificial intelligence, is a hot and important research topic now. And the fuzzy logic system and neural network system are the important research topics of artificial Intelligence with uncertainty. When we create many uncertain intelligent systems the transcendental knowledge, for example the known rules of the fuzzy logic systems and the training patterns of the neural network systems, should usually be prepared for the systems. The uncertainty of the transcendental knowledge, which can be thought of as errors from the viewpoint of mathematics and perturbation from dynamics, causes the complexity and solving difficulty of the things. Effective acquisition the transcendental knowledge, in which there has been much work but there is not pivotal breakthrough, and evaluation and controlling the errors of the transcendental knowledge impacting the performance of the systems, which has gotten some achievement in traditional fuzzy inference and fuzzy neural network but has not begun in universal neural network, have been two important research topics of the uncertain intelligent systems. The main work in this paper is as follows:1. This paper studies the influence of the uncertainty of patterns perturbation on the performance of Gauss Radial Basis Function Neural Network. It gives interrelated definitions and lemmas, points the usual instances of the training patterns perturbation and then theoretically and strictly proves that the outputs perturbation of some patterns perturbation of the Gauss Radial Basis Function Neural Network is no bigger than the training patterns perturbation. Lastly it gives an experiment in MATLAB validating the validity of the theoretically inferred result. This part of work gives a new viewpoint in studying the performance of the Gauss Radial Basis Function Neural Network.2. A method, which can automatically searches for the most or secondary optimized fuzzy control rules set according the controlling object of the systems, automatically acquiring fuzzy rules based on the genetic algorithm is proposed in this paper. An experiment illustrates the procedure of acquiring rules by genetic algorithm in pendulum system. The simulation results demonstrate the feasibility and effective-... |