SKEDSOFT

Neural Network & Fuzzy Systems

Description:-

  •  Competitive learning lacks the capability to add new clusters when deemed necessary.
  • Competitive learning does not guarantee stability in forming clusters. If the learning rate η is constant, then the winning unit that responds to a pattern may continue changing during training.
  • If the learning rate η is decreasing with time, it may become too small to update cluster centers when new data of differentprobability are presented.

Carpenter and Grossberg (1998) referred such occurrence as the stability-plasticity dilemma which is common in designing intelligent learning systems. In general, a learning system should be plastic, or adaptive in reacting to changing environments, and should be stable to preserve knowledge acquired previously.

Stability-Plasticity Dilemma (SPD)

Every learning system faces the plasticity-stability dilemma. The plasticity-stability dilemma poses few questions :

  • How can we continue to quickly learn new things about the environment and yet not forgetting what we have already learned?
  • How can a learning system remain plastic (adaptive) in response to significant input yet stable in response to irrelevant input?
  • How can a neural network remain plastic enough to learn new patterns and yet be able to maintain the stability of the already learned patterns?
  •  How does the system know to switch between its plastic and stable modes.
  •  What is the method by which the system can retain previously learned information while learning new things.

Answer to these questions, about plasticity-stability dilemma in learning systems is the Grossberg’s Adaptive Resonance Theory (ART).

  • ART has been developed to avoid stability-plasticity dilemma in competitive networks learning.
  • The stability-plasticity dilemma addresses how a learning system can preserve its previously learned knowledge while keeping its ability to learn new patterns.
  • ART is a family of different neural architectures. ART architecture can self-organize in real time producing stable recognition while getting input patterns beyond those originally stored.