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Introduction to Soft Computing
Soft computing is an approach to engineering that is inspired by nature. It includes techniques like fuzzy logic, probabilistic reasoning, evolutionary computation, neural networks, and machine learning. These techniques are useful for problems that are too complex or undefined for conventional analytical or hard computing techniques. Soft computing provides approximate solutions and can handle imprecise data. It has applications in areas like robotics, artificial intelligence, and machine translation.
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Overview of soft computing, its unpredictable nature, and its connection to human cognition.
Soft computing was proposed by Lotfi Zadeh in 1973, marking the beginning of its evolution.
Key components include Fuzzy Logic, Probabilistic Reasoning, Evolutionary Computation, Machine Learning, and Neural Networks.
Fuzzy Logic allows values to range between true and false, not limited to binary outcomes.
Uses logic and probability to manage uncertain situations, focusing on output variations.
Involves selecting solutions through trial and error among various options.
Machine Learning enables systems to learn independently, bypassing traditional algorithms.
Discusses the workings and types of neurons, their connectivity, topologies, and learning methods.
Outlines differences based on failure tolerance, time, cost, and imprecision values.
Applications in robotics, AI, translation services, sensory tech, and gaming.
A placeholder for a specific example relating to soft computing.
Highlights learning capabilities, automatic task sharing, and reduced algorithm needs.
Challenges in understanding neural networks and the time required for neuron connectivity.
Soft computing is more flexible and powerful than traditional algorithms for task execution.
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Introduction to Soft Computing
- 1. Soft Computing By: R.Geethikaramani 13JT1A0524
- 2. Contents: Introduction Historyof Soft Computing Components of Soft Computing Soft Computing vs hard computing Applications Example Merits Demerits Conclusion
- 3. Introduction: Soft computing(SC) solutions are unpredictable, uncertain and between 0 and 1. Human brain is the basic concept of Soft Computing.
- 4. History of SoftComputing: First proposed by Lotfi Zadeh in 1973. Later development made in soft computing.
- 5. Components of SoftComputing: Fuzzy Logic Probabilistic Reasoning Evolutionary Computation Machine Learning Neural Networks
- 6. Fuzzy Logic: Notonly formal boolean values, but also range between them 0.60
- 7. Probabilistic Reasoning: Probabilisticreasoning is using logic and probability to handle uncertain situations. Prob:"X" O/P:1 O/P:2 O/P:n
- 8. Evolutionary Computation: Choosingan exact solution from variety of solutions. Technically they belong to the family of trial and error problem solvers. Prob:"X" O/P:2
- 9. Machine Learning: Thisis a process of learning a system by itself without any algorithm.
- 10. Neural Networks: Working ofBiological Neuron: Working of Artificial Neuron:
- 11. Connection types ofNeurons: • Static Connection: • Dynamic Connection:
- 12. Topologies of Neurons: •Single-level Topology: • Multi-level Topology: • Recurrent:
- 13. Learning Methods ofNeurons: Supervised learning Unsupervised learning Reinforcement
- 14. Soft computing VsHard Computing: A small "FAILURE" Time Cost Imprecision values
- 15. Applications: Robotics Googletranslate Artificial intelligence Sensor based technologies Latest gaming technology
- 16.
- 17. Merits: Work bylearning. Work will be automatically shared. No need to write any algorithms
- 18. Demerits: Needs tounderstand before working with neural networks. Takes large time for connecting neurons
- 19. Conclusion: On behalf-of writingalgorithm for each and every specific task, Soft computing is very flexible and more powerful way to perform our task in easy way.