What do you mean by fuzzy neural network?
A fuzzy neural network is basically a neural network where the inputs as well as the connection weights are fuzzy numbers. From: Soft Computing in Textile Engineering, 2011.
What is fuzzy logic and neural network?
With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology.
How is fuzzy logic used in neural networks?
Fuzzy logic is largely used to define the weights, from fuzzy sets, in neural networks. When crisp values are not possible to apply, then fuzzy values are used. When we use fuzzy logic in neural networks then the values must not be crisp and the processing can be done in parallel.
What is fuzzy used for?
In medicine, fuzzy logic is used for computer-aided diagnoses, based on factors such as symptoms and medical history. In chemical distillation, fuzzy logic is used to control pH and temperature variables.
What are the drawbacks of fuzzy logic?
Disadvantages of Fuzzy Logic in Artificial Intelligence
- The accuracy of these systems is compromised as the system mostly works on inaccurate data and inputs.
- There is no single systematic approach to solve a problem using Fuzzy Logic.
- Due to inaccuracy in results, they are not always widely accepted.
Why is fuzzy neural network needed?
Fuzzy sets can be regarded as weights whereas the input and output variables and the rules are modeled as neurons. Thus the optimization of these functions in terms of generalizing the data is very important for fuzzy systems. Neural networks can be used to solve this problem.
Why is fuzzy logic important?
Fuzzy logic allows for the inclusion of vague human assessments in computing problems. New computing methods based on fuzzy logic can be used in the development of intelligent systems for decision making, identification, pattern recognition, optimization, and control.
Why neural networks are fuzzy?
Combining fuzzy systems with neural networks They can be used for solving a problem (e.g. pattern recognition, regression or density estimation) if there does not exist any mathematical model of the given problem. If the knowledge is incomplete, wrong or contradictory, then the fuzzy system must be tuned.
Why do we need fuzzy logic?
What are the characteristics of Neuro Fuzzy?
Characteristics. 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.