NDLI: On geometric constraint solving based on the immune neural network

Content Provider	IEEE Xplore Digital Library
Author	Cao Chunhong Zhang Bin Wang Limin Li Wenhui
Copyright Year	2008
Description	Author affiliation: Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang (Cao Chunhong; Zhang Bin)
Abstract	The geometric constraint solving is a popular problem in the current constraint design research. A constraint can describe a relation to be satisfied. Once the user defines a series of relations, the system will select a proper state to satisfy the constraints after the parameters are modified. Firstly we transform the constraint problem into an optimization problem. And then we propose a novel network framework combining the immune mechanism with the neural information - immune neural network to solve the geometric constraint problems. The users can make use of the characteristic information of the problem to be solved by the network and the network can be simplified by infusing the transcendental knowledge to adjust the the inspiriting function of the concealed layer unit so that the work efficiency and accuracy can be improved. The experiment can indicate that the immune neural network is not only feasible but also effective. It can simplify the framework applied in the concrete problem of the original model and it has a good work capability.
Starting Page	536
Ending Page	540
File Size	297432
Page Count	5
File Format	PDF
ISBN	9787900719706
DOI	10.1109/CHICC.2008.4605359
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute Of Intelligence M&C Technical Of KMUST
Subject Keyword	Design automation Antibody Geometric constraint solving Artificial neural networks Electronic mail Neural network Antigen Information science Problem-solving Artificial intelligence Immune system Inmmune algorithm Immune neural network
Content Type	Text
Resource Type	Article

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