Characterization of Internal Learning Parameters in Artificial Neural Networks

As modern computers become even more powerful, scientists continue to be challenged to use machines effectively for tasks that are relatively simple for humans. Based on examples, together with some feedback from a "teacher", we learn easily to recognize the letter A or distinguish a cat from a bird. More experience allows us to refine our responses and improve our performance. Although eventually, we may be able to describe rules by which we can make more decisions, these do not necessarily reflect the actual process we use. Even without a teacher we can group similar patterns together. Yet another common human activity is trying to achieve a goal that involves maximizing a resource (time with one's family, for example) while satisfying certain constraints (such as need to earn a living). Each of these types of problems illustrates tasks for which computer solutions may be sought. Traditional, sequential, logic based digital computing excels in many areas, but has been less successful for other types of problems. The development of artificial neural networks began approximately 50 years ago, motivated by a desire to try both to understand the brain and to emulate some of its strengths. [1]