Application of Artificial Neural Networks

by Andrii LebedynskiyKharkiv National Automobile and Highway University

Warren McCulloch (16.11.1898 – 24.10.1969)

Warren Sturgis McCulloch was an American neurophysiologist and cybernetician, known for his work on the foundation for certain brain theories and his contribution to the movement cybernetics.

He is remembered for his work with Joannes Gregorius Dusser de Barenne from Yale and later with Walter Pitts from the University of Chicago. There he provided the foundation for certain brain theories in a number of classic papers, including "A Logical Calculus of the Ideas Immanent in Nervous Activity"

Walter Pitts (23 April 1923 – 14 May 1969)

Walter Harry Pitts, Jr. was a logician who worked in the field of cognitive psychology.

He proposed landmark theoretical formulations of neural activity and emergent processes that influenced diverse fields such as cognitive sciences and psychology, philosophy, neurosciences, etc.

He is best remembered for having written along with Warren McCulloch, a seminal paper entitled "A Logical Calculus of Ideas Immanent in Nervous Activity" (1943).

Artificial neural networks are computational models inspired by animal central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition.

Commonly, though, a class of statistical models will be called «neural» if they consist of sets of adaptive weights, i.e. numerical parameters that are tuned by a learning algorithm, and are capable of approximating non-linear functions of their inputs.

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.The advantages include:

- Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.- Self-Organization: An ANN can create its own organization or representation of the information it receives during learning time.- Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.- Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.

The artificial neural networks are used in almost all spheres of life: in financial, in medical, in HR management, in science etc. For example one of the most common usages of artificial neural networks is pattern recognition.

Automatic (machine) recognition, description, classification, and grouping of patterns are important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. A pattern could be a fingerprint image, a handwritten cursive word, a human face, or a speech signal. The recognition problem here is being posed as a classification or categorization task, where the classes are either defined by the system designer (in supervised classification) or are learned based on the similarity of patterns (in unsupervised classification).

The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task.

They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture.

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Thank you for your attention!