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The chapter presents the details of fuzzyFuzzy logic, which is capable of handling challenges due to incomplete, unquantifiableUnquantifiable, and non-obtainable information. The topics that are part of this chapter were fuzzification and defuzzification, adaptive neuro-fuzzy inferenceInference systemAdaptive Neuro-Fuzzy Inference System (ANFIS), fuzzy cognitiveCognitive mapping, optimizationOptimization, and fuzzyFuzzy optimization. A detailed discussion of various membership functionsMembership functions (MF) is presented. It also briefly discusses fuzzy Convolutional Neural NetworksNetwork and fuzzyFuzzy Long Short-TermMemory MemoryLong Short-Term Memory (LSTM). The reader is expected to acquire a wealth of information about the described algorithmsAlgorithms, which tackle uncertaintyUncertainty in the data.

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Fuzzy-Based Modelling Algorithms

  • 章节

  • 发表日期：

    2025年

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  • Komaragiri Srinivasa Raju,
  • Dasika Nagesh Kumar

摘要

The chapter presents the details of fuzzyFuzzy logic, which is capable of handling challenges due to incomplete, unquantifiableUnquantifiable, and non-obtainable information. The topics that are part of this chapter were fuzzification and defuzzification, adaptive neuro-fuzzy inferenceInference systemAdaptive Neuro-Fuzzy Inference System (ANFIS), fuzzy cognitiveCognitive mapping, optimizationOptimization, and fuzzyFuzzy optimization. A detailed discussion of various membership functionsMembership functions (MF) is presented. It also briefly discusses fuzzy Convolutional Neural NetworksNetwork and fuzzyFuzzy Long Short-TermMemory MemoryLong Short-Term Memory (LSTM). The reader is expected to acquire a wealth of information about the described algorithmsAlgorithms, which tackle uncertaintyUncertainty in the data.