Download Intelligent Computing Theories: 9th International by Yu Zhang (auth.), De-Shuang Huang, Vitoantonio Bevilacqua, PDF

By Yu Zhang (auth.), De-Shuang Huang, Vitoantonio Bevilacqua, Juan Carlos Figueroa, Prashan Premaratne (eds.)

This ebook constitutes the refereed convention complaints of the ninth overseas convention on clever Computing, ICIC 2013, held in Nanning, China, in July 2013. The seventy four revised complete papers offered have been conscientiously reviewed and chosen from various submissions and are geared up in topical sections on neural networks, nature encouraged computing and optimization, cognitive technology and computational neuroscience, wisdom discovery and information mining, evolutionary studying and genetic algorithms computer studying concept and techniques, traditional language processing and computational linguistics, fuzzy conception and versions, delicate computing, unsupervised and strengthened studying, clever computing in finance, clever computing in petri nets, clever info fusion and data defense, digital truth and computing device interplay, clever computing in trend attractiveness, clever computing in picture processing, clever computing in robotics, complicated structures idea and methods.

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Intelligent Computing Theories: 9th International Conference, ICIC 2013, Nanning, China, July 28-31, 2013. Proceedings

This publication constitutes the refereed convention complaints of the ninth foreign convention on clever Computing, ICIC 2013, held in Nanning, China, in July 2013. The seventy four revised complete papers provided have been conscientiously reviewed and chosen from a number of submissions and are prepared in topical sections on neural networks, nature encouraged computing and optimization, cognitive technological know-how and computational neuroscience, wisdom discovery and information mining, evolutionary studying and genetic algorithms desktop studying idea and techniques, average language processing and computational linguistics, fuzzy thought and types, tender computing, unsupervised and strengthened studying, clever computing in finance, clever computing in petri nets, clever information fusion and knowledge protection, digital truth and laptop interplay, clever computing in trend reputation, clever computing in picture processing, clever computing in robotics, advanced structures concept and strategies.

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Additional resources for Intelligent Computing Theories: 9th International Conference, ICIC 2013, Nanning, China, July 28-31, 2013. Proceedings

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But since it only deals with one aspect of uncertainty, processed on the grey concept (imperfect information), so existence of their limitations. In many actualities, the qualitative grey judgment caused by imperfect information and randomness are symbiotic, the grey number usually is a random number within a certain range. While the traditional whitenization weight function is too rigid, and it cannot flexible descript the grey concept. In recent years, many scholars have begun to discuss the improvement of traditional whitenization weight function, for example in discussion, Dr.

Cooperative Behavior Acquisition for Mobile Robots in Dynamically Changing Real Worlds via Vision-based Reinforcement Learning and Development. In: Artificial Intelligence, pp. 275–292 (1999) 3. : Cooperative Behavior Acquisition in Multi-agent Reinforcement Learning System Using Attention Degree. S. ) ICONIP 2012, Part III. LNCS, vol. 7665, pp. 537–544. Springer, Heidelberg (2012) 4. : Characteristics of Behavior of Robots with Emotion Model. IEEJ Transaction on Electronics, Information and Systems (EIS) 124(7), 1390–1395 (2004) 5.

7 Mutation Mechanism Each Pwj ( gen) in probability matrix Pmatrix ( gen) changes gradually from initial values to 0 or 1 when gen increases to a large value. This means the population diversity and exploration ability are decreased with the increase of gen . Inspired by Bajula’s work [10], we propose a mutation operator to disturb the probability matrix. Denote α m ( gen) the mutation rate at generation gen and set α m ( gen) = α ( gen) / 2 . The process of the proposed mutation mechanism can be described as follows: Step 1: Randomly select u, v and q, where u ≠ v ≠ q .

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