Introduction to soft computing neuro fuzzy and genetic algorithms pdf

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introduction to soft computing neuro fuzzy and genetic algorithms pdf

Soft Computing - Dr. Savita Kumari Sheoran

In the field of artificial intelligence , neuro-fuzzy refers to combinations of artificial neural networks and fuzzy logic. Neuro-fuzzy hybridization results in a hybrid intelligent system that synergizes these two techniques by combining the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks. Neuro-fuzzy system the more popular term is used henceforth incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules. The main strength of neuro-fuzzy systems is that they are universal approximators with the ability to solicit interpretable IF-THEN rules. The strength of neuro-fuzzy systems involves two contradictory requirements in fuzzy modeling: interpretability versus accuracy. In practice, one of the two properties prevails. The neuro-fuzzy in fuzzy modeling research field is divided into two areas: linguistic fuzzy modeling that is focused on interpretability, mainly the Mamdani model ; and precise fuzzy modeling that is focused on accuracy, mainly the Takagi-Sugeno-Kang TSK model.
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Soft Computing Tools / Paradigm : Fuzzy Logic, Neural Network, Evolutionary Computing Explained

Request PDF on ResearchGate | On Jun 3, , Udit Chakraborty and others published introduction to Soft Computing: Neuro-Fuzzy and Genetic Algorithms.

Introduction to Soft Computing Neuro-fuzzy and Genetic Algorithms by Samir Roy & Udit Chakraborthy

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Introduction 1. Fuzzy Sets 2. Probability 2. Fuzzy Logic 3. Fuzzy Inference Systems 4.



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