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An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. [1][2] Since it integrates both neural networks and fuzzy logic principles, it has potential to capture ...
Adaptive resonance theory. Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information. It describes a number of artificial neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.
Fuzzy cognitive maps are signed fuzzy directed graphs. Spreadsheets or tables are used to map FCMs into matrices for further computation. FCM is a technique used for causal knowledge acquisition and representation, it supports causal knowledge reasoning process and belong to the neuro-fuzzy system that aim at solving decision making problems, modeling and simulate complex systems. [4]
Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict starfish and sea urchins, which are correlated with "nodes" that represent visual features. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and ...
Recurrent neural networks (RNNs) are a class of artificial neural networks for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.
There are more examples of AGA variants: Successive zooming method is an early example of improving convergence. [26] In CAGA (clustering-based adaptive genetic algorithm), [ 27 ] through the use of clustering analysis to judge the optimization states of the population, the adjustment of pc and pm depends on these optimization states.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Fusion adaptive resonance theory (fusion ART)[1][2] is a generalization of self-organizing neural networks known as the original Adaptive Resonance Theory [3] models for learning recognition categories across multiple pattern channels. There is a separate stream of work on fusion ARTMAP, [4][5] that extends fuzzy ARTMAP consisting of two fuzzy ...