What is neuro-fuzzy technique?

What is neuro-fuzzy technique?

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. Fuzzy logic based tuning of neural network training parameters.

What are the drawbacks of fuzzy logic and neural networks?

A major drawback of Fuzzy Logic control systems is that they are completely dependent on human knowledge and expertise. You have to regularly update the rules of a Fuzzy Logic control system. These systems cannot recognize machine learning or neural networks.

What are hybrid systems in Soft Computing?

Hybrid systems: A Hybrid system is an intelligent system that is framed by combining at least two intelligent technologies like Fuzzy Logic, Neural networks, Genetic algorithms, reinforcement learning, etc. These systems are capable of reasoning and learning in an uncertain and imprecise environment.

What is neural trained fuzzy logic?

Neural-Trained Fuzzy Logic New patterns of data can be learned easily with the help of neural networks hence, it can be used to preprocess data in fuzzy systems. Neural network, because of its capability to learn new relationship with new input data, can be used to refine fuzzy rules to create fuzzy adaptive system.

What is neural fuzzy network?

A fuzzy neural network or neuro-fuzzy system is a learning machine that finds the parameters of a fuzzy system (i.e., fuzzy sets, fuzzy rules) by exploiting approximation techniques from neural networks.

What is the classification of neuro-fuzzy hybrid?

The HFNS has a hierarchical topology consisting of three kinds of building blocks-fuzzy neural networks (FNNs), neural networks (NN) and fuzzy systems (FS). The FNNs and NNs are based on the feedforward backpropagation model.

What is neuro-fuzzy controller?

A neuro-fuzzy controller is used in robots in order to obtain the desired movements on them, that is, reaching a final position getting from an initial position.

How does a neuro-fuzzy inference system work?

4.2 Adaptive Neuro-Fuzzy Inference System. ANFIS is an integration system in which neural networks are applied to optimize the fuzzy inference system. ANFIS constructs a series of fuzzy if–then rules with appropriate membership functions to produce the stipulated input–output pairs.

What is the classification of Neuro-Fuzzy hybrid?

Which two technologies are combined for Neuro-Fuzzy hybrid systems?

Hybrid systems combining fuzzy inference system and artificial neural networks are proving their effectiveness in a wide variety of real world problems.

What are the features of neuro-fuzzy spectrum?

A neuro-fuzzy system can be viewed as a 3-layer feedforward neural network. The first layer represents input variables, the middle (hidden) layer represents fuzzy rules and the third layer represents output variables. Fuzzy sets are encoded as (fuzzy) connection weights.