Neuro-fuzzy, the Glossary
In the field of artificial intelligence, the designation neuro-fuzzy refers to combinations of artificial neural networks and fuzzy logic.[1]
Table of Contents
19 relations: Algorithm, Artificial intelligence, Cluster analysis, Concept drift, Connectionism, Data stream mining, Defuzzification, Fuzzy control system, Fuzzy logic, Fuzzy rule, Fuzzy set, Hybrid intelligent system, IEEE Transactions on Fuzzy Systems, Indicator function, Neural network (machine learning), Radial basis function, Self-organizing map, Universal approximation theorem, Unsupervised learning.
Algorithm
In mathematics and computer science, an algorithm is a finite sequence of mathematically rigorous instructions, typically used to solve a class of specific problems or to perform a computation.
Artificial intelligence
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.
See Neuro-fuzzy and Artificial intelligence
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters).
See Neuro-fuzzy and Cluster analysis
Concept drift
In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model.
See Neuro-fuzzy and Concept drift
Connectionism
Connectionism (coined by Edward Thorndike in the 1931) is the name of an approach to the study of human mental processes and cognition that utilizes mathematical models known as connectionist networks or artificial neural networks.
See Neuro-fuzzy and Connectionism
Data stream mining
Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records.
See Neuro-fuzzy and Data stream mining
Defuzzification
Defuzzification is the process of producing a quantifiable result in crisp logic, given fuzzy sets and corresponding membership degrees. Neuro-fuzzy and Defuzzification are fuzzy logic.
See Neuro-fuzzy and Defuzzification
Fuzzy control system
A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively). Neuro-fuzzy and fuzzy control system are fuzzy logic.
See Neuro-fuzzy and Fuzzy control system
Fuzzy logic
Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1.
See Neuro-fuzzy and Fuzzy logic
Fuzzy rule
Fuzzy rules are used within fuzzy logic systems to infer an output based on input variables. Neuro-fuzzy and fuzzy rule are fuzzy logic.
See Neuro-fuzzy and Fuzzy rule
Fuzzy set
In mathematics, fuzzy sets (also known as uncertain sets) are sets whose elements have degrees of membership. Neuro-fuzzy and fuzzy set are fuzzy logic.
Hybrid intelligent system
Hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence subfields, such as.
See Neuro-fuzzy and Hybrid intelligent system
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems is a bimonthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society.
See Neuro-fuzzy and IEEE Transactions on Fuzzy Systems
Indicator function
In mathematics, an indicator function or a characteristic function of a subset of a set is a function that maps elements of the subset to one, and all other elements to zero.
See Neuro-fuzzy and Indicator function
Neural network (machine learning)
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. Neuro-fuzzy and neural network (machine learning) are artificial neural networks.
See Neuro-fuzzy and Neural network (machine learning)
Radial basis function
In mathematics a radial basis function (RBF) is a real-valued function \varphi whose value depends only on the distance between the input and some fixed point, either the origin, so that \varphi(\mathbf). Neuro-fuzzy and radial basis function are artificial neural networks.
See Neuro-fuzzy and Radial basis function
Self-organizing map
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data. Neuro-fuzzy and self-organizing map are artificial neural networks.
See Neuro-fuzzy and Self-organizing map
Universal approximation theorem
In the mathematical theory of artificial neural networks, universal approximation theorems are theorems of the following form: Given a family of neural networks, for each function f from a certain function space, there exists a sequence of neural networks \phi_1, \phi_2, \dots from the family, such that \phi_n \to f according to some criterion. Neuro-fuzzy and universal approximation theorem are artificial neural networks.
See Neuro-fuzzy and Universal approximation theorem
Unsupervised learning
Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data.
See Neuro-fuzzy and Unsupervised learning
References
[1] https://en.wikipedia.org/wiki/Neuro-fuzzy
Also known as Fuzzy neural network.