Real - World Applications of Neural Network

Neural Network technology performs "intelligent" tasks similar to those performed by the human brain. Today, many researchers are investigating Neural Networks, the network holds great potential as the front - end of expert system that require massive amount of inputs from sensor as well as real - time response. Neural Networks has been successfully applied to broad spectrum of data - intensive applications, such as; Process modeling and control, Machine diagnosis, Medical diagnosis, Voice Recognition, Financial forecasting, Fraud detection. In this paper presentation, real - world applications of neural network was considered including "Traveling Salesman Problem Routes". Elements of an Artificial Neural System (ANS), Characteristics of (ANS), Historical Developments in (ANS) Technology, Applications of (ANS) Technology, Commercial Development in (ANS), Neural Networks versus conventional computers, etc was also given due consideration. There was a new development in programming paradigm, which arose in the 1980's. This new development was based on how the human brain processes 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 (neurons) working in unison to solve specific problems. It was sometimes called connectionism since it models solution to problem by training simulated neurons connected in a network. Neural Network has proven to be 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 Network achieved this by; acquiring knowledge via learning and sharing the learnt Knowledge within inter - neuron connection strengths generally know as "Synaptic Weights". An Artificial Neural Network (ANN), usually called Neural Network has been found to hold great potential as the front - end of expert system and have made remarkable success in providing real time response to complex pattern recognition problems. Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer "what if" questions. Other reasons why we make use of Neural Networks include; Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.