Xem 1-5 trên 5 kết quả Multiobjective complexity
  • Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning.

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  • This book aims at attracting the interest of researchers and practitioners around the applicability of meta-heuristic algorithms to practical scenarios arising from different knowledge disciplines. Emphasis is placed on evolutionary algorithms and swarm intelligence as computational means to efficiently balance the tradeoff between optimality of the produced solutions and the complexity derived from their estimation.

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  • The current control problems present natural trend of increasing its complexity due to performance criteria that is becoming more sophisticated. The necessity of practicers and engineers in dealing with complex dynamic systems has motivated the design of controllers, whose structures are based on multiobjective constraints, knowledge from expert, uncertainties, nonlinearities, parameters that vary with time, time delay conditions, multivariable systems, and others.

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  • The current control problems present natural trend of increasing its complexity due to performance criteria that is becoming more sophisticated. The necessity of practicers and engineers in dealing with complex dynamic systems has motivated the design of controllers, whose structures are based on multiobjective constraints, knowledge from expert, uncertainties, nonlinearities, parameters that vary with time, time delay conditions, multivariable systems, and others.

    pdf0p aries23 29-09-2012 35 9   Download

  • 4 The New Terms of Reference for Science for Governance: Postnormal Science This chapter addresses the epistemological implications of complexity. In fact, according to what has been discussed so far, hard science, when operating within the reductionist paradigm, is not able to handle in a useful way the set of relevant perceptions and representations of the reality used by interacting agents, which are operating on different scales. No matter how complicated, individual mathematical models cannot be used to represent changes on a multi-scale, multiobjective performance space.

    pdf21p hoakimthienduong 21-12-2011 28 4   Download

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