The Application of Artificial Neural Networks in Engineering and Finance Nicholas christakis Department of physics University of Crete DCABES. October 2009
DCABES, October 2009 The Application of Artificial Neural Networks in Engineering and Finance Nicholas Christakis Department of Physics University of Crete
Presentation Outline Basics of artificial Neural Networks(ANNs pplication of Ann in rotorcraft aerodynamics Application of ANN in the prediction of stocks prices short-term trading DCABES. October 2009
DCABES, October 2009 Presentation Outline • Basics of Artificial Neural Networks (ANNs) • Application of ANN in rotorcraft aerodynamics • Application of ANN in the prediction of stocks prices – short-term trading
Presentation Outline Basics of Artificial Neural Networks (ANNS Application of ann in rotorcraft aerodynamics Application of ANN in the prediction of stocks prices short-term trading DCABES. October 2009
DCABES, October 2009 Presentation Outline • Basics of Artificial Neural Networks (ANNs) • Application of ANN in rotorcraft aerodynamics • Application of ANN in the prediction of stocks prices – short-term trading
Presentation Outline Basics of Artificial Neural Networks (ANNS pplication of Ann in rotorcraft aerodynamics pplication of ann in the prediction of stock prices short-term trading DCABES. October 2009
DCABES, October 2009 Presentation Outline • Basics of Artificial Neural Networks (ANNs) • Application of ANN in rotorcraft aerodynamics • Application of ANN in the prediction of stock prices – short-term trading
Basics of anns DCABES. October 2009
DCABES, October 2009 Basics of ANNs
Basics of anns ANNS: Information processing machines, inspired by the way biological nervous systems work ANNs composed of Simple processing elements(neurons Connected together working in unison to solve specific problems ANN learn by example and generalize well on unseen data detect trends that are too complex to be noticed by either humans or other computer techniques deal well with situations where the inputs are erroneous. incomplete or muzzy DCABES. October 2009
DCABES, October 2009 Basics of ANNs • ANNs: Information processing machines, inspired by the way biological nervous systems work. • ANNs composed of: – Simple processing elements (neurons) – Connected together – working in unison to solve specific problems. • ANNs – learn by example and generalize well on unseen data. – detect trends that are too complex to be noticed by either humans or other computer techniques. – deal well with situations where the inputs are erroneous, incomplete or fuzzy
Basics of anns Comparisons between Biological and Artificial Networks Human brain 100 109 neurons with 1000 connection paths dendrites)per neuron 100 10 2 interconnections sec All work in parallel 100 10 2 computations/sec Serial computer 10 computations DCABES. October 2009
DCABES, October 2009 Basics of ANNs Comparisons between Biological and Artificial Networks • Human brain – 100 109 neurons with 1000 connection paths (dendrites) per neuron 100 1012 interconnections / sec – All work in parallel 100 1012 computations/sec • Serial computer – 107 computations / sec Human brain 10 106 times faster than a serial computer
Basics of anns anns may be used as Autonomous predictive tools Pre-processors for numerical process models in order to determine unknown parameters from data sets DCABES. October 2009
DCABES, October 2009 Basics of ANNs ANNs may be used as: – Autonomous predictive tools – Pre-processors for numerical process models in order to determine unknown parameters from data sets
Basics of anns Generic Operation of ANNs Train the network with a given dataset to recognize patterns within it Decide after how many epochs(full cycles through the whole of the dataset) the network is adequately trained Network is operational for predicting(from a given set of inputs)outputs it has not been trained for Main ann characteristics Network Architecture(how the network is set up earning algorithm(how the network learns DCABES. October 2009
DCABES, October 2009 Basics of ANNs Generic Operation of ANNs ➢ Train the network with a given dataset to recognize patterns within it ➢ Decide after how many epochs (full cycles through the whole of the dataset) the network is adequately trained ➢ Network is operational for predicting (from a given set of inputs) outputs it has not been trained for Main ANN characteristics ➢ Network Architecture (how the network is set up) ➢ Learning Algorithm (how the network learns)
Basics of anns Generic layout of an ANN 3 types of layers present-input, intermediate(hidden), output ALL nodes(neurons) of a layer connected to all nodes of neighbouring layers Input layer Hidden Layers Output Layer Feed Forward network- information signal al ways propagates in the forward direction DCABES. October 2009
DCABES, October 2009 Basics of ANNs Generic layout of an ANN • 3 types of layers present-input, intermediate (hidden), output • ALL nodes (neurons) of a layer connected to ALL nodes of neighbouring layers Feed Forward network- information signal always propagates in the forward direction
