Adaptive resonance associative map
نویسندگان
چکیده
منابع مشابه
Adaptive resonance associative map
-This article introduces a neural architecture termed Adaptive Resonance Associative Map ( ARAM) that extends unsupervised Adaptive Resonance Theory (ART) systems for rapid, yet stable, heteroassociative learning. ARAM can be visualized as two overlapping ART networks sharing a single category field. Although ARAM is simpler in architecture than another class o f supervised ART models known as ...
متن کاملAssociative Self-organizing Map
We present a study of a novel variant of the Self-Organizing Map (SOM) called the Associative SelfOrganizing Map (A-SOM). The A-SOM is similar to the SOM and thus develops a representation of its input space, but in addition it also learns to associate its activity with the activity of one or several external SOMs. The A-SOM has relevance in e.g. the modelling of expectations in one modality du...
متن کاملAdaptive bidirectional associative memories.
Bidirectionality, forward and backward information flow, is introduced in neural networks to produce two-way associative search for stored stimulus-response associations (A(i),B(i)). Two fields of neurons, F(A) and F(B), are connected by an n x p synaptic marix M. Passing information through M gives one direction, passing information through its transpose M(T) gives the other. Every matrix is b...
متن کاملMap Recall based on Hierarchical Associative Memories
During recent years, artificial neural networks turned to be quite popular even in areas like cartography or navigation where processing of huge amounts of high-dimensional spatial data is needed. In this context, the data may represent geographical maps, plans of buildings, etc., which lead us straight to use similar ideas for autonomous devices operation and control. When a person moves along...
متن کاملThe Time Adaptive Self Organizing Map for Distribution Estimation
The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Networks
سال: 1995
ISSN: 0893-6080
DOI: 10.1016/0893-6080(94)00092-z