Artificial Neural Networks ( Ann )

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CHAPTER 5 Artificial Neural Networks (ANN) 5.1 Machine Learning In machine learning, systems are trained to infer patterns from observational data. A particularly simple type of pattern, a mapping between input and output, can be learnt through a process called supervised learning. A supervised-learning system is given training data consisting of example inputs and the corresponding outputs, and comes up with a model to explain those data (a process called function approximation). It does this by choosing from a class of model specified by the system’s designer. [Nature. ANN 4] 5.1.1 Machine Learning Applied to the Air Engine The rapid growth of data sets means that machine learning can now use complex model classes and tackle highly non-trivial inference problems. Such problems are usually characterized by several factors: The data are multi-dimensional; the underlying pattern is complex (for instance, it might be nonlinear or changeable); and the designer has only weak prior knowledge about the problem in particular, a mechanistic understanding is lacking. [Nature, ANN 4] 5.2 Overview of ANN Artificial Neural Networks (ANN) are a branch of the field known as "Artificial Intelligence" (AI) which may also consists of Fuzzy logic (FL) and Genetic Algorithms (GA). ANN are based on the basic model of the human brain with capability of generalization and learning. The purpose of this simulation to the simple model of human neural cell is to acquire the intelligent
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