What is a Neural Network?
A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform “intelligent” tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following two ways: Click Here for a Video Presentation • A neural network acquires knowledge through learning. • A neural network’s knowledge is stored within inter-neuron connection strengths known as synaptic weights. The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes to modeling data that contains non-linear characteristics. The most common neural network model is the multilayer perceptron (M
A neural network is a biologically-motivated computational construct. A network may be hardware or software based, and consists of several nodes, or neurons, connected by weighted communication lines. A neural network is a structure whose ith neuron has input value x_i, output value y_i = g(x_i) and connections to other neurons described by weights w_ij. The envelope function g(x_i) is commonly a sigmoidal function, g(x) = 1/(1+e^x). The input value x_i of neuron i is given by the formula x_i=Sum(j != i) w_ij y_j. We use a feed-forward network, in which the neurons are organized into layers: an input layer, hidden layer(s), and an output layer. The input layer input values are set by the environment, while the output layer output values are returned to the environment (see figure below). The output information may be interpreted as a control signal, for example. The hidden layers have no external connections: they only have connections with other layers in the network. In a feed-forwar
In contrast to standard regression models one can readily create in MS Excel, neural networks perform nonlinear regression modeling. They produce a collection of simple nonlinear functions that mutually feed data to each other in a way that resembles some types of neural brain cells. Typically, the user never cares to know what these functions are, just as long as the neural net does its intended job. But if you are really interested, go to our SPECIAL REPORTS department for papers you can download regarding the use of neural networks suitable for futures forecasting. The user does not need to program neural nets to work. They ‘program’ themselves by learning from examples of historical information. When you later apply new data to them, the neural net creates a new forecast. Be careful not to confuse neural networks (NN) with another artificial intelligence paradigm called expert systems(ES). ES programs are designed to mimic the rational thinking of an expert. However, if the expert
Neural Networks are a different paradigm for computing: • von Neumann machines are based on the processing/memory abstraction of human information processing. • neural networks are based on the parallel architecture of animal brains. Neural networks are a form of multiprocessor computer system, with • simple processing elements • a high degree of interconnection • simple scalar messages • adaptive interaction between elements A biological neuron may have as many as 10,000 different inputs, and may send its output (the presence or absence of a short-duration spike) to many other neurons. Neurons are wired up in a 3-dimensional pattern. Real brains, however, are orders of magnitude more complex than any artificial neural network so far considered. Example: A simple single unit adaptive network: The network has 2 inputs, and one output. All are binary.
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.
