05-04-2012, 02:28 PM
NEURAL NETWORKS & ITS APPLICATIONS
ARTIFICIAL NEURAL NETWORKS REPORT.doc (Size: 4.08 MB / Downloads: 87)
Introduction and Motivation
The brain performs astonishing tasks: we can walk, talk, read, write, recognize hundreds of faces and objects, irrespective of distance, orientation and lighting conditions, we can drink from a cup, give a lecture, drive a car, do sports, we can take a course in neural networks at the university, and so on. Well, it’s actually not the brain, it’s entire humans that execute the tasks. The brain plays, of course, an essential role in this process, but it should be noted that the body itself, the morphology (the shape or the anatomy, the sensors, their position on the body), and the materials from which it is constructed also do a lot of useful work in intelligent behavior. In other words, the brain is always embedded into a physical system that interacts with the real world, and if we want to understand the function of the brain we must take embodiment into account.
Differences between Computers & Brains
The point of this comparison is to show that we might indeed benefit by employing
brain-style computation.
• Parallelism
Computers function, in essence, in a sequential manner, whereas brains are massively parallel. Moreover, the individual neurons are densely connected to other neurons: a neuron has between just a few and 10,000 connections. Of particular interest is the observation that parallelism requires learning or some other developmental processes. In most cases it is not possible to either set the parameters (the weights, see later) of the network manually, or to derive them in a straightforward way by means of a formula: a learning process is required.
• Nonlinearity
Neurons are highly nonlinear, which is important particularly if the underlying physical mechanism responsible for the generation of the input signal is inherently (e.g. speech signal) nonlinear. Recently, in many sciences, there has been an increasing interest in non-linear phenomena. If a system – an animal, a human, or a robot – is to cope with nonlinearities, non-linear capacities are required. Many examples of such phenomena
will be given throughout the class.
• Plasticity
Learning and adaptivity are enabled by the enormous neural plasticity which is illustrated e.g. by the experiment of Melchner, in which the optic nerves of the eyes of a ferret were connected to the auditory cortex which then developed structures similar to the visual one.
Introduction to Neurons
An animal brain is made up of a network of cells called neurons, coupled to receptors and effectors. Neurons are intimately connected with glial cells, which provide support functions for neural networks.
The Learning Rule
As already pointed out, weights are modified by learning rules. The learning rules determine how ”experiences” of a network exert their influence on its future behavior. There are, in essence, three types of learning rules: supervised, reinforcement, and non-supervised or unsupervised.
Supervised learning
The term supervised is used both in a very general and narrow technical sense. In the narrow technical sense supervised means the following. If for a certain input the corresponding output is known, the network is to learn the mapping from inputs to outputs. In supervised learning applications, the correct output must be known and provided to the learning algorithm. The task of the network is to find the mapping. The weights are changed depending on the magnitude of the error that the network produces at the output layer:
Applications of ARTIFICIAL NEURAL NETWORKS
Artificial Life: Galapagos
Galapagos is a fantastic and dangerous place where up and down have no meaning, where rivers of iridescent acid and high-energy laser mines are beautiful but deadly artifacts of some other time. Through spatially twisted puzzles and bewildering cyber-landscapes, the artificial creature called Mendel struggles to survive, and you must help him.
Mendel is a synthetic organism that can sense infrared radiation and tactile stimulus. His mind is an advanced adaptive controller featuring Non-stationary Entropic Reduction Mapping -- a new form of artificial life technology developed by Anark. He can learn like your dog, he can adapt to hostile environments like a cockroach, but he can't solve the puzzles that prevent his escape from Galapagos.