2.3 Artificial Neural Networks

The structure of artificial neural networks was based on the present understanding of biological neural systems. The computation is achieved by dense interconnection of simple processing units. To describe the attributes of computing, the artificial neural networks go by many names such as connectionist models, parallel distributed processors, or self-organizing system. With such features, an artificial neural system has great potential in performing applications such as speech and image recognition where intense computation can be done in parallel and the computational elements are connected by weighted links.

The artificial neuron, the most fundamental computational unit, is modeled based on the basic property of a biological neuron. This type of processing unit performs in two stages: weighted summation and some type of nonlinear function. It accepts a set of inputs to generate the weighted sum, then passes the result to the nonlinear function to make an output.

Unlike conventional computing systems, which has fixed instructions to perform specific computations, the artificial neural network needs to be taught and trained to function correctly. The advantage is that the neural system can learn new input-output patterns and adjust the system parameters. Such learning can eliminate specifying instructions to be executed for computations. Instead, users simply supply appropriate sample input-output patterns to the network [Zur92].

The model of the entire artificial neural network is determined by the network topology, type of neural model, and learning rules. These are the main interests in designing artificial neural networks.