Neural Network: - An 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.
u The objective of a neural network is to transform input into meaningful output.
Fuzzy Logic or Fuzzy Set Theory: - Fuzzy means not clear, distinct, precise or blurred (with unclear outline).
u It is a flexible machine learning technique.
u Fuzzy logic deals with uncertainty or vagueness existing in a system and formulating fuzzy rules to find a solution to problems.
u Fuzzy logic use values between 0 and 1.
u Fuzzy set also consist of Fuzzy rule base to perform approximate reasoning somewhat similar to the human brain.
u Example of Fuzzy logic: - For automatic breaking system the traditional values are taken as either 0 or 1.
Central driving force for the creation of hybrid soft computing systems:
Every soft computing technique has particular computational parameters which make them suited for a particular problem and not for others.
o ability to learn & decision making
u Neural Networks are good at recognizing patterns but they are not good at explaining how they reach their decisions.
u Fuzzy logic is good at explaining the decisions but cannot automatically acquire the rules used for making the decision.
u These limitations act as a central driving force for the creation of hybrid soft computing systems where two or more techniques are combined in a suitable manner that overcomes the of individual techniques.
u The aim is to build highly automated, intelligent machines for the future generations using all of these techniques.
Hybrid System: - A Hybrid Intelligent System is one that combines at least two intelligent technologies.
u For example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system.
u Hybridization: The main aim of the concept of hybridization is to overcome the weakness in one technique while applying it and bringing out the strength of the other technique to find a solution by combining them.
Neuro-fuzzy hybrid system (NFHS): - Proposed by J.S.R Jang (Jyh-Shing Roger Jang) in the early '90s.
u Neuro-Fuzzy hybridization is widely termed as Fuzzy Neural Network (FNN) or Neuro-Fuzzy System(NFS).
u NFHS is a learning mechanism that utilizes the training and learning algorithms from neural networks to find parameters of a fuzzy system.
u Neuro-Fuzzy Hybrid System is a combination of fuzzy system and neural network.
u The human-like reasoning style of fuzzy systems is incorporated by the use of
. Fuzzy sets
. Linguistic model
. along with IF-THEN fuzzy rules
Comparison of Fuzzy Systems with Neural Networks
A black box is a device, system or object which can be viewed in terms of its inputs and outputs without any knowledge of its internal workings.
The architecture of the neuro-fuzzy hybrid system
Ø The architecture is a three-layer feedforward neural network model.
Ø First layer corresponds to the input variables.
Ø Second layer corresponds to the fuzzy rules.
Ø Third layer corresponds to the output variables.
Classification of the neuro-fuzzy hybrid system
u NFS are classified into the following two categories:
1. Cooperative NFSs
2. General Neuro-fuzzy hybrid systems
Cooperative NFS: Fuzzy system is governed by fuzzy IF-THEN rules.
u A fuzzy system is a set of fuzzy rules that convert inputs to outputs.
u A fuzzy rule is defined as a conditional statement in the form: IF x is A. THEN y is B. where x and y are linguistic variables; A and B are linguistic values determined by fuzzy sets on the universe of discourse X and Y, respectively.
u Linguistic variables: variable whose values are words or sentences rather than numbers. For E.g., Speed, Temp. and service.
u Linguistic value: Values or terms used to describe the linguistic variables. For E.g., for speed (slow, fast, faster), for temperature (cool, warm, hot), for service (good, poor, excellent.).
u IF temperature IS hot THEN Speed up fan.
u ANN attempt to learn parameters from the fuzzy system.
u FNN learns "fuzzy set" from the given "training data". This is done by fitting membership function with a neural network; fuzzy sets then being determined offline.
u A membership function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1.
General Neuro-fuzzy hybrid systems:
u Architecture of General Neuro-Fuzzy Hybrid Systems
u Advantage of general NFHS: there is no communication between NN and FS.
u The rule base of fuzzy system is assumed to be a NN.
u Fuzzy sets are regarded as weights.
u And the rules and input, output variables as Neurons.
u The choice to include or discard neurons can be made
In learning step.
u Fuzzy knowledge base is represented by neurons of NN.
Ø Membership functions expressing the linguistic terms Of the inference rule should be formulated for building Fuzzy controller.
Advantages of Neuro-fuzzy hybrid systems:
u It can handle any kind of information (numeric, linguistic, logical, etc.)
u It can manage imprecise, partial, vague or imperfect information.
u It has self-learning, self-organizing and self-tuning capabilities.
u It doesn’t need prior knowledge of relationships of data.
Areas of Applications for the use of Hybrid System:
u Engineering Design
u Stock market analysis and prediction
u Medical diagnosis
u Process control
u Credit card analysis
u Few cognitive simulations
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
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