Neural Networks Research Paper Example | Topics and Well Written Essays - 1000 words

Neural Networks - Research Paper ExampleThis paper presents an overview of the Artificial Neural Networks (ANNs). This paper will draw the basic idea, some history and uses of ANN. Basically an artificial neural net profit is a collection of programs of data structures that is almost capable of the process of the human brain. In this scenario, a neural network encompasses a wide variety of processors that work collaboratively, each with its own small sphere of facts and makes use of the data in its topical anaesthetic memory. In addition, an ANN can be considered as a computer program or simulation that is built through a wide variety of absolved and extremely unified processing components that are capable of managing information by means of their active state reaction to inputs. In fact, artificial neural networks contribute the capability to solve those problems that are difficult to resolve using traditional processes and techniques, as well as sometime a lot of them have be en tackled through neural networks, for instance forecasting grassland community, analysis of seismic signals or solar radiation, control of chaotic dynamical systems, data and air quality control and smorgasbord of remotely sensed information (Benvenuto & Marani, 2000 Giles, 1998 Laudon & Laudon, 1999). According to (Stergiou & Siganos, 2010), an artificial neural network is a data processing model that is inspired by the method of biological nervous systems information processing. Neural Network is also a renowned and heavily researched area of the AI. Stergiou & Siganos (2010) described that in the neural network field the on the job(p) of human neuron is copied into the computer based systems. In addition, the neural network is an AI based computer program which imitates the human nervous systems working. It is based on neural net which has weights on each node that can be adjusted to make suitable decisions. Such systems are typically seen in self-winding driving systems tha t are under-research and at the early stages of development (Stergiou & Siganos, 2010 Laudon & Laudon, 1999). The history of the artificial neural networks can be traced back to the 1940s with the introduction of the first neural network computing model, which was presented by McCulloch and Pitts. In the 1950s, Rosenblatts work got a considerable attention of researchers. Additionally, this work was further resulted in a two-layer network, the perceptron, which was able to learn various classifications by making changes to connection weights. Without a doubt, the