Neuro Fuzzy System |Soft Computing| ~xRay Pixy
Neuro-fuzzy hybrid system tutorial |Soft Computing|
Neuro-Fuzzy Hybrid System (NFHS) - Soft Computing (Neural Network)
An introduction to the Neuro-Fuzzy System.
A hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence
This video contains information about Neural networks, fuzzy logic, neurons, biological neural network and different types of Hybrid Systems.
Topics Covered in this video:
1.) Neuro-Fuzzy System?
- Introduction to Neural Network, Fuzzy Logic or Fuzzy set theory, and Hybrid System.
- Example of Neural Network.
- Diagram of Neural Network.
- How does Neural Network Works?
- Difference between Fuzzy Logic and Traditional Logic.
2.) What is a Neural Network?
- A neural network, in general, is a highly interconnected network of a large number of processing elements called Neurons in an architecture inspired by the human brain.
3.) What are Fuzzy Logic and Traditional Logic?
- Fuzzy logic deals with uncertainty or vagueness existing in a system and formulating fuzzy rules to find a solution to problems.
4.) What are the processing elements and neurons?
5.) Example of Neural Networks.
6.) Application of Neural Network.
Neural Networks are good at recognizing patterns but they are not good at explaining how they reach their decisions.
7.) Examples of fuzzy logic.
- Automatic braking system
8.) Different types of Hybrid systems in Soft computing.
- Neuro-Fuzzy Hybrid System
- Neuron genetic hybrid system
- Fuzzy Genetic Hybrid Systems
9.) Neuro-Fuzzy Hybrid System (NFHS)
- What is Neuro-Fuzzy Hybrid System?
- Creation of a hybrid soft computing system.
- Various Types of hybrid systems.
- Neuro-Fuzzy Hybrid System
- Neuro Genetic Hybrid system.
- Fuzzy Genetic Hybrid system.
10.) Applications of Neuro-Fuzzy Hybrid System.
- Different Areas of Applications for the use of Hybrid Systems.
- The architecture of the Neuro-Fuzzy Hybrid System.
11.) Central Driving Forces Behind Neuro-Fuzzy Hybrid System.
12.) Comparison between Neural network and biological network.
13.) Model of Neural Network.
14.) Layers of the Neural network hidden layer, input layer, and output layer.
Artificial Intelligence subfields as:
• Neuro-fuzzy systems
• hybrid connectionist-symbolic models
• Fuzzy expert systems
• Connectionist expert systems
• Evolutionary neural networks
• Genetic fuzzy systems
• Rough fuzzy hybridization
What is Neuro-Fuzzy Hybrid System - Soft Computing
https://youtu.be/IfaQWblqADw
Artificial Neural Networks - Soft Computing ~xRay Pixy
How do Artificial Neural Networks Learn?
https://youtu.be/ec4uvXiQP5A
Neuro-Fuzzy System |Hybrid System| Soft Computing ~xRay Pixy
In the field of artificial intelligence, neuro-fuzzy refers to combinations of artificial neural networks and fuzzy logic. Neuro-Fuzzy Hybrid System is a combination of Neural Network 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 hybridization is widely termed as the fuzzy neural network (FNN) or neuro-fuzzy system (NFS) in the literature. 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.
Strength of NFHS: The strength of neuro-fuzzy systems involves two contradictory requirements in fuzzy modeling: interpretability versus accuracy.
• The learning procedure is constrained to ensure the semantic properties of the underlying fuzzy system.
• A neuro-fuzzy system based on an underlying fuzzy system is trained by means of a data-driven learning method derived from neural network theory. This heuristic only takes into account local information to cause local changes in the fundamental fuzzy system.
Special Three-Layer:
o The first layer corresponds to the input variables.
o The second layer symbolizes the fuzzy rules.
o The third layer represents the output variables.
Genetic fuzzy systems are fuzzy systems constructed by using genetic algorithms or genetic programming, which mimic the process of natural evolution, to identify its structure and parameter.
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