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Titlebook: Computational Intelligence; Principles, Techniqu Amit Konar Textbook 2005 Springer-Verlag Berlin Heidelberg 2005 Multi-agent system.artific

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樓主: DEIGN
11#
發(fā)表于 2025-3-23 10:01:25 | 只看該作者
Matrix elements for spherical Gaussians,of ADALINE neurons has been presented. The time required for training the neural net is insignificantly small. The scheme for the recognition of objects from their gray level images, using fuzzy ADALINE model, is translation-, rotation- and size- invariant.
12#
發(fā)表于 2025-3-23 15:53:14 | 只看該作者
Fuzzy Databases and Possibilistic Reasoning, fuzzy relational databases. The chapter finally employs the above two concepts in the design of fuzzy relational databases. The concepts outlined in the chapter have been illustrated with many examples.
13#
發(fā)表于 2025-3-23 20:56:34 | 只看該作者
Competitive Learning Using Neural Nets, scope of realization of competition by Hebbian learning and the way-out to handle the limitation of Hebbian learning by Oja’s principle have been discussed in detail. The chapter also introduced principal component analysis and self-organizing feature models and examined their applications in face recognition problem.
14#
發(fā)表于 2025-3-23 23:04:10 | 只看該作者
Object Recognition from Gray Images Using Fuzzy ADALINE Neurons,of ADALINE neurons has been presented. The time required for training the neural net is insignificantly small. The scheme for the recognition of objects from their gray level images, using fuzzy ADALINE model, is translation-, rotation- and size- invariant.
15#
發(fā)表于 2025-3-24 05:34:33 | 只看該作者
16#
發(fā)表于 2025-3-24 09:54:08 | 只看該作者
17#
發(fā)表于 2025-3-24 14:23:19 | 只看該作者
Benjamin Dufée,Etienne Mémin,Dan Crisanaries. Thus a pattern may be classified into one or more classes with a certain degree of membership to belong to each class. The algorithm for fuzzy pattern recognition is numerically illustrated, and its application in object recognition from real time video frames is also presented.
18#
發(fā)表于 2025-3-24 16:27:31 | 只看該作者
Game Behavior Within the Intersection,ct of the chapter is the derivation of the classical back-propagation learning algorithm from the principles of gradient descent learning. The chapter ended with discussions on Radial Basis Function neural nets and modular neural nets.
19#
發(fā)表于 2025-3-24 20:59:43 | 只看該作者
An Introduction to Computational Intelligence, the synergistic behavior of neuro-fuzzy, neuro-GA, neuro-belief and fuzzy-belief network models is also included in the chapter. A list of tutorial problems is appended at the end of the chapter to build up students’ ability in handling real world problems.
20#
發(fā)表于 2025-3-25 02:41:06 | 只看該作者
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