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Applying Computational Intelligence: How to Create Value - download pdf or read online

By Arthur Kordon

ISBN-10: 3540699104

ISBN-13: 9783540699101

ISBN-10: 3540699139

ISBN-13: 9783540699132

In concept, there is not any distinction among idea and perform. yet, in perform, there's. Jan L. A. van de Snepscheut The ?ow of educational rules within the sector of computational intelligence has penetrated with great velocity and patience. hundreds of thousands of functions have proved the sensible strength of fuzzy common sense, neural networks, evolutionary com- tation, swarm intelligence, and clever brokers even prior to their theoretical origin is totally understood. And the recognition is emerging. a few software program owners have stated the recent desktop studying gold rush to “Transfer information into Gold”. New buzzwords like “data mining”, “genetic algorithms”, and “swarm optimization” have enriched the pinnacle executives’ vocabulary to cause them to glance extra “visionary” for the twenty first century. The word “fuzzy math” grew to become political jargon after getting used through US President George W. Bush in a single of the election debates within the crusade in 2000. Even strategy operators are discussing the perf- mance of neural networks with an analogous ardour because the functionality of the Dallas Cowboys. even if, for many of the engineers and scientists introducing computational intelligence applied sciences into perform, taking a look at the growing to be variety of new ways, and realizing their theoretical ideas and power for price construction turns into a a growing number of dif?cult task.

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One possible solution to this problem is to rely on other methods to reduce the complexity by extracting the important variables and records. Using statistical methods, neural networks, and support vector machines can significantly reduce the dimensionality of the problem. An example of an integrated technology which accomplishes this goal is given in Chap. 11. e. it is valid only for a specific case. For example, the membership function, representing the phrase “slightly decreased power” can be significantly different even between two similar pumps.

In a similar way, the learning knowledge in the artificial neural network can be represented by the numerical weights of their mathematical links. In the same way as biological neurons learn new patterns by readjusting the synapse strength based on positive or negative experience, artificial neural networks learn by readjustment of the numerical weights based on a defined fitness function. 3 Support Vector Machines Support vector machines (SVM) deliver learning capabilities derived from the mathematical analysis of statistical learning theory.

Liebowitz, If you are a dog lover, build expert systems; if you are a cat lover, build neural network, Expert Systems with Applications, 21, p. 63, 2001 Chapter 2 A Roadmap Through the Computational Intelligence Maze Signposts can turn a highway into labyrinth. Stanisław Jerzy Lec The objective of the first part of the book is to give the reader a condensed overview of the main computational intelligence methods, such as fuzzy systems, neural networks, support vector machines, evolutionary computation, swarm intelligence, and intelligent agents.

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Applying Computational Intelligence: How to Create Value by Arthur Kordon

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