Fuzzy neuro-computational technique and its application to modelling and control

M. Gupta, M. Gorzałczany
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引用次数: 14

Abstract

The authors present a model building technique which combines the strength of the fuzzy set theory and the neural network based structures. This technique can simultaneously deal with two types of knowledge, a nonfuzzy one and a fuzzy one, which usually describe the behavior of complex processes. The proposed method can also be directly applied to the construction of a new type of intelligent fuzzy controller. Some aspects of the adequacy of this fuzzy neuro-computational model are also discussed. A numerical example is provided.<>
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模糊神经计算技术及其在建模和控制中的应用
作者提出了一种结合模糊集理论和基于神经网络结构的模型构建技术。该技术可以同时处理两种类型的知识,即非模糊知识和模糊知识,这两种知识通常描述复杂过程的行为。该方法也可直接应用于新型智能模糊控制器的构建。本文还讨论了模糊神经计算模型的充分性。给出了一个数值算例。
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Control of a robotic manipulating arm by a neural network simulation of the human cerebral and cerebellar cortical processes Neural network training using homotopy continuation methods A learning scheme of neural networks which improves accuracy and speed of convergence using redundant and diversified network structures The abilities of neural networks to abstract and to use abstractions Backpropagation based on the logarithmic error function and elimination of local minima
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