(23rd-January-2021)
• Development of Neural Networks date back to the early 1940s. It experienced an upsurge in popularity in the late 1980s. This was a result of the discovery of new techniques and developments and general advances in computer hardware technology.
• Some NNs are models of biological neural networks and some are not, but historically, much of the inspiration for the field of NNs came from the desire to produce artificial systems capable of sophisticated, perhaps intelligent, computations similar to those that the human brain routinely performs, and thereby possibly to enhance our understanding of the human brain.
• Most NNs have some sort of training rule. In other words, NNs learn from examples (as children learn to recognize dogs from examples of dogs) and exhibit some capability for generalization beyond the training data.
• Neural computing must not be considered as a competitor to conventional computing. Rather, it should be seen as complementary as the most successful neural solutions have been those which operate in conjunctionwith existing, traditional techniques.
• Computers have to be explicitly programmed
• Analyze the problem to be solved.
• Write the code in a programming language.
• Neuralnetworks learn from examples
• No requirement of an explicit description of the problem.
• No need for a programmer.
• The neural computer adapts itself during a training period, based on examples of similar problems even without a desired solution to each problem. After sufficient training the neural computer is able to relate the problem data to the solutions, inputs to outputs, and it is then able to offer a viable solution to a brand new problem.
• Able to generalize or to handle incomplete data.
Comments