Adaptive Resonance Theory
This network was developed by Stephen Grossberg and Gail Carpenter in 1987. It is based on competition and uses unsupervised learning model. Adaptive Resonance Theory (ART) networks, as the name…
Learning Vector Quantization
Learning Vector Quantization (LVQ), different from Vector quantization (VQ) and Kohonen Self-Organizing Maps (KSOM), basically is a competitive network which uses supervised learning. We may define it as a process…
Unsupervised Learning
As the name suggests, this type of learning is done without the supervision of a teacher. This learning process is independent. During the training of ANN under unsupervised learning, the…
Supervised Learning
As the name suggests, supervised learning takes place under the supervision of a teacher. This learning process is dependent. During the training of ANN under supervised learning, the input vector is presented…
Learning and Adaptation
As stated earlier, ANN is completely inspired by the way biological nervous system, i.e. the human brain works. The most impressive characteristic of the human brain is to learn, hence…
Artificial Neural Network – Building Blocks
Processing of ANN depends upon the following three building blocks − Network TopologyAdjustments of Weights or LearningActivation Functions In this chapter, we will discuss in detail about these three building…
Artificial Neural Network – Basic Concepts
Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve performance) to do tasks by considering examples, generally without task-specific programming.…
Distributed Reasoning
Introduction There are many reasons why we might want to implement or adopt a distributed approach to reasoning in an AI system: Humans do it -- psychological models.Parallel machines exist…
Bayesian networks
These are also called Belief Networks or Probabilistic Inference Networks. Initially developed by Pearl (1988). The basic idea is: Knowledge in the world is modular -- most events are conditionally independent of most other events.Adopt…
Dempster-Shafer Models
This can be regarded as a more general approach to representing uncertainty than the Bayesian approach. Bayesian methods are sometimes inappropriate: Let A represent the proposition Demi Moore is attractive. Then…
