Evolution_of_Soft_Computing_fuzzy_logic.pptx

Evolution_of_Soft_Computing_fuzzy_logic.pptx

• 1.
  Evolution of SoftComputing Key Concepts, Differences, Requirements, and Applications
• 2.
  Introduction • Soft computingis a collection of methodologies that aim to exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, and low-cost solutions. • Components: • - Fuzzy Logic (FL) • - Neural Networks (NN) • - Genetic Algorithms (GA)
• 3.
  Evolution of SoftComputing • - 1980s: Emergence of fuzzy logic and neural networks. • - 1990s: Integration of evolutionary algorithms and probabilistic reasoning. • - 2000s: Hybrid systems combining multiple soft computing techniques. • - Present: Applications in AI, data science, robotics, etc.
• 4.
  Difference Between Hardand Soft Computing • Hard Computing: • - Relies on binary logic and precise algorithms. • - Deterministic and exact solutions. • - Requires rigid problem formulation. • Soft Computing: • - Deals with approximate and uncertain solutions. • - Tolerates imprecision and uncertainty.
• 5.
  Requirement of SoftComputing • Key Drivers: • - Growing complexity of real-world problems. • - Need for adaptable and robust systems. • - Limitations of traditional computing methods. • Why Soft Computing? • - Handles uncertainty and partial truth effectively.
• 6.
  Applications of SoftComputing • - Artificial Intelligence and Machine Learning: Natural Language Processing, Image Recognition. • - Robotics: Autonomous Navigation, Adaptive Control Systems. • - Medical Diagnosis: Disease Prediction, Personalized Treatment Plans. • - Optimization Problems: Supply Chain Management, Scheduling Systems. • - Finance: Stock Market Prediction, Fraud
• 7.
  Case Studies • -Case 1: Medical Diagnosis System • - Use of fuzzy logic for detecting diseases with imprecise symptoms. • - Case 2: Autonomous Vehicles • - Neural networks for real-time decision- making.
• 8.
  Advantages and Challenges •Advantages: • - High adaptability. • - Cost-effective solutions. • - Robustness to uncertainty. • Challenges: • - Requires domain expertise for effective implementation. • - Computationally intensive for large datasets.
• 9.
  Conclusion • Summary: • -Soft computing provides flexible and robust solutions for modern problems. • - Integration of techniques enhances its capability. • Future Outlook: • - Growing importance in AI, IoT, and data- driven industries.
• 10.
  Q&A • Feel freeto ask questions!