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From Simple to Complex: How Does the ART System Evolve Diverse Neural Networks?
In the field of artificial intelligence, Adaptive Resonance Theory (ART) has gradually attracted attention as a model for exploring brain information processing. Founded by Stephen Grossberg and Gail Carpenter, this theory provides a series of artificial neural network models that use supervised and unsupervised learning methods to process patterns. Identification and prediction issues. The core concept of ART is that the recognition and cognition of objects are usually the result of the interaction between "top-down" observation expectations and "bottom-up" sensory information.
The ART model assumes that "top-down" expectations exist in the form of memory templates or prototypes, which are compared with the actual features of the perceived object.
This comparison produces a measure of the degree of categorical properties, and as long as the difference between perception and expectation does not exceed a set threshold called the "alerting parameter", the perceived object will be regarded as being of the expected category. member. The ART system thus proposes a solution to the "plasticity/stability" problem, namely, incremental learning while acquiring new knowledge without destroying existing knowledge.
Learning Model
The basic ART system is an unsupervised learning model, which usually consists of a comparison field and a recognition field, and contains neurons, alert parameters, and a reset module. The comparison field accepts an input vector and transfers it to the neuron in the recognition field that best matches it. The optimal neuron for this match outputs a negative signal, which inhibits other neurons, so that the recognition field exhibits the characteristics of lateral inhibition, allowing each neuron to represent a category.
After completing the classification of the input vector, the reset module will compare the strength of the recognition match with the alert parameters and decide whether to start training based on the result.
If the recognition match exceeds the alert parameters, training will begin and the weights of the winning recognition neurons will be adjusted; if it fails to cross, a search process will be carried out to continuously disable active recognition neurons until a match that meets the alert parameters is found. This process and its effects are significantly affected by vigilance parameters, with high vigilance parameters producing detailed memories and low vigilance parameters producing more general memories.
Training Methods
There are two main training methods for ART-based neural networks: slow learning and fast learning. Slow learning methods use differential equations to calculate how much the weights should be adjusted, depending on how long the input vector is present; fast learning uses algebraic equations to calculate the required weight changes.
While fast learning is efficient and effective in many tasks, slow learning methods are more biologically plausible and can be used for continuous-time networks.
Different types of ART systems
During the evolution of ART, different types have emerged, such as ART 1 focusing on binary input and ART 2 supporting continuous input. ART 2-A is a streamlined version of ART 2, with a significant increase in running speed. ART 3 is based on ART 2 and simulates the regulation of synaptic activity by external neurotransmitters, providing a more physiologically plausible mechanism to partially inhibit the category that produces mismatch reset.
In addition to the basic ART types, there are other more complex structures, such as Fuzzy ART, Fusion ART and TopoART, which are extensions for multiple mode channels such as sound and image.
Challenges of ART Systems
However, the categories learned by Fuzzy ART and ART 1 are significantly affected by the order in which the training data are processed. Even using slower learning rates, this effect could not be completely eliminated and was thought to be a side effect of the mechanism that ensures stable learning for both networks. Newer and more advanced ART networks such as TopoART and Hypersphere TopoART provide a solution without considering the order in which the categories are established.
These networks can be summarized into clusters, where the shape of the clusters is not affected by the order in which the relevant categories are created.
With the advancement of science and technology and the academic community's continued in-depth research on ART theory, the application and improvement of this model is still ongoing. How will future ART systems be able to further adapt to complex environments to promote the development of intelligent technology?
