Читать книгу Handbook of Intelligent Computing and Optimization for Sustainable Development - Группа авторов - Страница 45

Learning is an impressive characteristic of human brain. But, the research on exact process of learning in human nervous system is still in its primary stage. The scientists claim that, learning in biological system occurs due to some alteration of neural structure and synaptic connections. ANN replicates the learning process of human nervous system and the ability to learn is one of the most unique features of ANN. The learning algorithms adjust the weighted connections between the neurons of neural network. The sequence of events occurs during learning process is as follows:

1 1. The environment stimulates the network in which it is embedded.

2 2. As a result of stimulation, the free parameters of the network get altered.

3 3. The system generates response to the environment in an enhanced way.

The purpose of the learning algorithm is to find an appropriate set of weight matrices so that it can generate output efficiently after mapping any input. The three classes of learning processes are listed below.

1 1. Supervised Learning: In this case, the learning algorithm provides the desired output along with the given input while training the ANN. Because of the input-output pair, the neural model becomes capable of calculating the error based on the target vs. actual output. Based on the calculated error, the model can be corrected by adjusting the weights.

2 2. Unsupervised Leaning: In unsupervised learning, the algorithm only feeds the set of input to the neural model and the weighted connection of the network is adjusted by internal monitoring system. The neural network finds some kind of pattern within the given input and accordingly the artificial is network is modified without any external assistance.

3 3. Reinforcement Learning: Reinforcement learning has some resemblance with supervised learning, but in this case no target output is given, instead, certain reward or penalties are given based on the performance of the neural model. It is a goal-oriented algorithm which receives the reward through trial-and-error method.

So far, we have given a brief overview of ANNs which a significant domain of artificial intelligence. In the next section, we will focus on the core area of this chapter, i.e., ANN using DNA computing. In the sphere of DNA computation, the logical aspect of artificial intelligence has been replaced by chemical properties and characteristics of DNA molecules.