 |
 |
|
|
Neural networks, more accurately called Artificial Neural Networks (ANN), are computational models that consist of a number of simple processing units that communicate by sending signals to each other over a large number of weighted connections. They were originally developed from the inspiration of human brains. In human brains, a biological neuron collects signals from other neurons through a host of fine structures called dendrites. The neuron sends out spikes of electrical activity through a long, thin stand known as an axon, which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connected neurons. When a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes. Like human brains, neural networks also consist of processing units (artificial neurons) and connections (weights) between them. The processing units transport incoming information on their outgoing connections to other units. The "electrical" information is simulated with specific values stored in those weights that make these networks have the capacity to learn, memorize, and create relationships amongst data. A very important feature of these networks is their adaptive nature where "learning by example" replaces "programming" in solving problems. This feature renders these computational models very appealing in application domains where one has little or incomplete understanding of the problems to be solved, but where training data are available. There are many different types of neural networks, and they are being used in many fields. And new uses for neural networks are devised daily by researchers. Some of the most traditional applications include [1][2]: · Classification – To determine military operations from satellite photographs; to distinguish among different types of radar returns (weather, birds, or aircraft); to identify diseases of the heart from electrocardiograms. · Noise reduction – To recognize a number of patterns (voice, images, etc.) corrupted by noise. · Prediction – To predict the value of a variable given historic values. Examples include forecasting of various types of loads, market and stock forecasting, and weather forecasting. The model built in this thesis falls into this category. |
|