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  • Today researchers need to solve vague defined problems working with huge data sets describing signals close to chaotic ones. Common feature of such signals is missigng algebraic model explaining their nature. Genetics Algorithms and Evolutionary Strategies are suitable to optimize such models and Genetic Programming Algorithms to develop them. Hierarchical GPA-ES algorithm presented herein is used to build compact models of difficult signals including signals representing deterministic chaos. Efficiency of GPA-ES is presented in the paper. Specific group of non-linearly composed functions similar to real biomedical signals is studien in the paper, On the base of these prerequisities, models applicable to complex biomedical signals like EEG modeling is formed and studied within the contribution.
  • Today researchers need to solve vague defined problems working with huge data sets describing signals close to chaotic ones. Common feature of such signals is missigng algebraic model explaining their nature. Genetics Algorithms and Evolutionary Strategies are suitable to optimize such models and Genetic Programming Algorithms to develop them. Hierarchical GPA-ES algorithm presented herein is used to build compact models of difficult signals including signals representing deterministic chaos. Efficiency of GPA-ES is presented in the paper. Specific group of non-linearly composed functions similar to real biomedical signals is studien in the paper, On the base of these prerequisities, models applicable to complex biomedical signals like EEG modeling is formed and studied within the contribution. (en)
Title
  • The Use of Local Models Optimized by Genetic Programming Algorithm in Biomedical-Signal Analysis
  • The Use of Local Models Optimized by Genetic Programming Algorithm in Biomedical-Signal Analysis (en)
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  • The Use of Local Models Optimized by Genetic Programming Algorithm in Biomedical-Signal Analysis
  • The Use of Local Models Optimized by Genetic Programming Algorithm in Biomedical-Signal Analysis (en)
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  • RIV/68407700:21260/12:00197317!RIV13-MSM-21260___
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  • 176376
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  • RIV/68407700:21260/12:00197317
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  • biosignals; chaos; genetic programming algorithm; evolutionary strategy (en)
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  • [E0D1E9FC9D2E]
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  • Heidelberg
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  • Intelligent systems reference library č. sv. 38
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  • Handbook of optimization From Classical to Modern Approach
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  • Brandejský, Tomáš
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  • Springer-Verlag
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  • 978-3-642-30503-0
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  • 21260
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