Artificial Neural Networks Sample Clauses

Artificial Neural Networks. Artificial neural networks (XXX) [9] is an effective and well-known tool for handling complicated and dynamic problems that involve lots of parameters. In view of its network architecture, XXX is also known as distributed processing system. An XXX model varies according to its architecture, learning algorithm and activation function. XXX has lots of applications in a variety of fields such as fault detection [10] and feature classification. XXX can help reduce the requirement of memory space via its memory capability. And its adaptive learning capability allows XXX to survive in the critical and

Artificial Neural Networks. Artificial Neural Networks are a specific subgroup of machine learning. They are a collection of computation techniques that function as non-standard statistical tools employed for pattern recognition within data (50). They encompass the same learning techniques as machine learning with networks able to perform supervised, unsupervised and reinforcement learning as appropriate. ANNs are a step forward in complexity from more traditional machine learning such as logistic regression. ANNs are typically able to deal with more heterogeneous, complex and even incomplete data sets with more success than traditional approaches. With each layer of complexity or plasticity that is added to a network, the computational power required to run each network goes up exponentially (82). Their real work applications were limited due to this computational restriction during the 1990’s and early 2000’s. Once data processing became faster and cheaper in the past 10 years, networks and their knock-on real work application have exploded into almost all aspect of modern life and science.